#but that's not seeing the forest for the trees in terms of actually analysing and understanding a soloist's work

[sanstropfremir]

Hey! I hops life has been treating you well! And warning for a long and convoluted ask incoming (which is pretty par for the course for me). I’ve been going down a Yunho (U-Know Yunho to be precise lol) rabbit hole and I’m curious how you think he compares to other male SM solo artists especially Taemin (though I think it would be interesting to compare all SM solo artists like BOA or Taeyeon and Hyoyeon for the purpose of this ask im just thinking about the dudes). Cause I feel like him, Taemin, and Kai all fall on different ends of the same spectrum that’s distinct from some of the other SM solos like the other Exos (except I might add Baekhyun in there but I’m just not familiar enough with him) as they tend to tackle that darker sexier sound. I’ve been thinking about how when people say a male solo artists is making taemin lite music it’s usually actually closer to Yunho’s more recent stuff. Like taemin has kind of become short hand for dark male solo concept especially if it’s on the sexier or high concept side when that’s not always an accurate comparison. I’ve seen this especially with Wonho which is really interesting to me cause in my mind he falls way more on the Yunho end of the spectrum than Taemin (by like a long shot). And this could also be cause the kpop circles I run in are way less familiar with Yunho to make that comparison in the first place, I was just wondering if others like you that knew Yunho would agree with me. I also found it interesting that Taemin went solo before Yunho even though TVXQ are the older group so I wonder he affected Yunho’s music at all. Cause Yunho kind of seems like a more, I don’t want to say masculine, definitely more of a player, conventionally sexy foil to taemin’s more unhinged (satanic?? Fantasy? Horror-adjacent? I can’t really describe it accurately but I mean TVXQ has down their share of that too) sensual side and I can’t tell if it’s cause of the age difference between them, their group concepts (I mean TVXQ are the grown men of the kpop industry in my mind and I have no doubt they play a role in inspiring any “manly” or mature concept and Yunho’s concepts seems to follow pretty closely to TVXQ’s image in a way that Taemin doesn’t at all with Shinee), or their own artist personas and careers- taemin has always been viewed as the “feminine” one and you’ve talked a lot about how he’s both rejected and embraced that image overtime in his career. And to further complicate this I don’t really know where to put Kai between them cause he has the more overt sex appeal in Mmmh, and in his image over all, that Yunho has but also is more sensual and softer (subtle?) dance style that seems closer to taemin (or at least taemin’s image I’ll be the first to admit that my understanding of him as an artists doesnt always align with his actual output and the expectations I project on him) so I guess he goes in the middle. But peaches does really stand out as a sort of softer lighter storybook concept (which I love cause I feel like we don’t see traditional concepts that are more feel good) so that kind of throws a wrench into my theory and I guess thats what I get for treating artists’ output as a monolith and for comparing them😅. This ask is getting very very long and I’m late for class so I won’t continue but I did have some idea about where Xiumin’s recent comeback fits into this roster of artists but I’ll save that for another day lol. As always thanks for your input and I hope this makes at least some sense to you!

i stopped photo editing to answer this lmao bc i think this a very interesting question/thought experiment etc etc

firstly, most people will make the taemin comparison for male soloist because he is significantly more well known as a soloist, and part of that is that he's famous for being a soloist; an extension of which is the disparity between his solo work and his group work. now there are two points i want to fork off into from here:

-> the first is that it's a flawed comparison to place yunho, taemin, and kai on a spectrum because they're not derivatives of each other in the way that a spectrum would imply; what they all are is derivatives of rain. every dance-based male soloist is, via some lineage, a descendant of rain, whether it's through the skills line (taemin) or the 'masculinity' line (yunho) or a mix (kai). baekhyun doesn't factor in here because he's almost purely rnb based and therefore a kangta derivative.

-> the second is that it is valid for people to be making that comparison to taemin because most younger idols are going to be using him as reference, and because a lot of these younger idols are following his method of creating a separate solo persona from their group. yunho does not have that distinction. he is quite literally the face of tvxq, and therefore inextricably bound to the group's identity because there are literally only two of them. if they were, for example, still a five member group, he might have had more a chance to diversify himself a bit, but that's a bygone point because we don't live in that world. the reason tvxq made it through the split and maintained being an extremely successful group is very likely because of yunho's singlehanded determination. obviously changmin does care about the group, but as we've seen with his solo work, he's much more of a musical chameleon; he's more focused on dabbling in widely different genres that interest him. yunho's solo music however, is pretty consistent in sound and also not all that surprising of a leap from who he is in tvxq. so by that observation, i would say there isn't actually a difference between uknow yunho and uknow the soloist. his artistic identity is tvxq, in a way that he can't separate, unlike how taemin can separate from shinee. and following that point through, no younger soloist can really be compared to him because his history and group image are so integral to what he does. that's why there's like a negative number of young idols that every attempt to cover a yunho song, bc the vibes of something like follow or thank u would OBLITERATE anyone under the age of 30.

if you were to look at just aesthetics and music styles, sure on the surface there are some younger idols that unintentionally skew more towards some of the stuff that yunho has done, but in the end that's because they're all under the rain umbrella.

#kpop questions #tvxq w #tvxq #yunho #taemin meta #i know i've talked about this before in a post somewhere. but yunho's only comparable peers are literally like. junsu and rain #both them COULD cover a yunho song if they wanted to. but again. their vibes are wrong. they don't have the same desperation #to be completely honest i actually think it's unimportant to compare the literal aesthetics/music style of solo artists #and instead you need to compare HOW they approach those aesthetics styles etc etc #like you could say that taemin kai yunho and baekhyun have all done dance based 'dark sexy' concepts within a relatively close timespan: #criminal (sept 2020) mmmh (nov 2020) thank u (jan 2021) and bambi (march 2021) #but that doesnt take into account the fact that all of them approach the concept of 'dark sexy' totally differently #and each according to their own contexts #i dont think you can put all of sm's soloists on a big scale of similarity of music and aesthetic #i mean. you can bc duh theyre all gonna look and sound similar bc they share resources #but that's not seeing the forest for the trees in terms of actually analysing and understanding a soloist's work #this is basically just a very longwinded way of me saying that i dont think there's anyone that makes solo work like yunho #i guess you could argue that kai is actually the most similar to yunho since he doesnt have a real distinction between solo kai and exo kai #but again its different bc he's got a wider array of genres he's willing to try and exo is not the same as tvxq #but again again its not about relating them to each other. its about relating them in comparison to rain #answers #text #like when i say taemin is more popular i mean like. 1.2 million monthly spotify listeners vs 30k. its not an insignificant difference

[thecinematicalgorithm]

Can't Catch Me Now: Lucy Gray and Katniss Story-Tie Analysis

I have not been on Tumblr much over the past couple of years but with the coming film The Ballad of Songbirds and Snakes on its way I knew I'd be dusting this old page off. The Hunger Games series is one of my all-time favorite book series and the films are some of my favorite book-to-film adaptations, so to say I am pumped for this upcoming movie is an understatement. And to top it all, I have been obsessing over Olivia Rodrigo's new song Can't Catch Me Now, which if you've read the prequel, you'll know that it perfectly ties Lucy Gray's story to Katniss' journey. As always I want to warn anyone who might read this that spoilers for the upcoming film and Suzanne Collins' novel lie ahead. Also fair warning, this is super long cause I don't know how to be concise.

In preparation for the prequel film I have re-read TBOSAS and I am currently re-reading the original THG series (I am currently on Catching Fire, if anyone cares to know lol). I am also planning on a movie marathon the week of the prequel release, which I fully intend on subjecting my boyfriend to as he recently admitted he has only ever seen the first (and I simply cannot let him continue living life with no clue on how wonderful Peeta Mellark is). With that said, I have had a few thoughts, which I wanted to share before the release knowing that I will certainly have more thoughts after I have seen the film.

Honestly, I am going to be ridiculously obsessed for the next several weeks. I also know I wrote a couple of analyses on the differences between Snow and Katniss and the early games vs. the later games, which I will re-post so people can read them if they haven't before.

Enough chit-chat though let's get to it.

The parallels between Katniss and Lucy Gray are quite extensive and beautiful despite the characters being in many ways polar opposites. There is the saying that "Lucy Gray Baird is a performer made to hunt while Katniss is a hunter made to perform". This is a great summary of their overall character profile, and while I may at some point do a breakdown of Lucy Gray vs Katniss, I first want to write about how Lucy Gray and Katniss' story are far more connected than some might have realized. Part of the realization for me actually came while listening to Olivia's new song.

The chorus of the song reads as:

But I'm in the trees, I'm in the breeze

My footsteps on the ground

You'll see my face in every place

But you can't catch me now

Through wading grass, the months will pass

You'll feel it all around

I'm here, I'm there, I'm everywhere

But you can't catch me now

No, you can't catch me now

In terms of Katniss, I think it's been obvious for sometime that Snow particularly despises Katniss because she is a strong reminder of Lucy Gray Baird. She is a girl from District 12. She stood out during her Reaping, and swept the Captiol off their feet during her time in his city. She not only sang in the arena, but she sang a young girl "to sleep" with the very song Lucy Gray sang Maude Ivory to sleep. She used the Captiol's berries to save herself and Peeta, just as Lucy Gray used the Captiol's snakes to save herself. She wears a Mockingjay pin, the very bird which Snow undoubtedly relates to Lucy Gray and rebellion (far before it truly became the symbol of rebellion).

Katniss may not be like Lucy Gray in personality, but to Snow, Lucy Gray's spirit must seem very much alive in Katniss, and just as he tried desperately to rid the forests surrounding District 12 of mockingjays, this is one Mockingjay he wants to destroy.

The second verse of the song goes:

Bet you thought I'd never do it

Thought it'd go over my head

I bet you figured I'd pass with the winter

Be something easy to forget

Oh, you think I'm gone 'cause I left

This verse summarizes Snow's mindset at the end of TBOSAS, as we know he thinks he is safe from the threat of Lucy Gray. Her games have been erased, as time passes "there will be a vague memory that a girl sang in the arena" and even that too shall pass. However, where he goes wrong is when he fails to understand the deep connection and love the other Covey share for Lucy Gray. Despite not seeing how the story ends for them, or even having a solid explanation of Lucy Gray's ending, we at least know that Lucy Gray and her songs were not wiped from existance. Whether they believed, as he supposed, that the mayor was responsible for Lucy Gray's disappearance does not erase their connection to her.

Snow may have chosen never to allow love to control him again, but he did not erase the love those children had for Lucy Gray. Her music became all they had left of her so you can bet they continued singing them and sharing them, even if they had to do it on the down low. (I also share the common fan theory that Maude Ivory is the grandmother to Katniss Everdeen, and I'm hoping the film confirms this). Either way, Katniss clearly learned those songs from somewhere, which for Snow would have been a siren's call from the great beyond that Lucy Gray did not pass with the winter and she was not as forgotten as he had hoped.

Then we go into the bridge of the song where Olivia sings:

You, you can't, you can't catch me now

I'm coming like a storm into your town

You can't, you can't catch me now

I'm higher than the hopes that you brought down (repeats)

This is my favorite part of the song. Not only is it moving and emotional but it ties so much of Lucy's story to Katniss'. Both girls were like storms in the Capitol, sweeping the people and the nation into their stories so they could not help but be invested. Both were near impossible to control, despite Snow's best efforts, and both had a spirit of hope greater than Snow's ability to crush the highest of hopes. There's also something deeper, which intended by Olivia or not, makes this song perfect for the series. The lines "I'm coming like a storm into your town" and "I'm higher than the hopes that you brought down" is sung from the point of view of Lucy Gray. Both bring to mind images of the rebellion in THG: Mockingjay. The rebels stormed into the Capitol and their hope was higher than the hopes and lives which Snow had already destroyed in an effort to quell the rebellion.

However, just like Katniss becoming the Mockingjay, or the symbol of the rebellion, Lucy Gray had become her music. She was the anthem of the rebellion. If Katniss inspired hope, Lucy Gray was that hope. The hope of freedom. Dead or not Lucy Gray was finally free and her song reflects that truth and the rebels clung to it. Dead or alive they would be free.

Furthermore, Lucy Gray's song not only led to the freedom of Panem, but it also led to the freedom of Peeta's mind from the lies and brainwashing inflicted on him in the Capitol. Remember, Katniss always associated Peeta with hope until Snow brainwashed him. And if you'll recall, Peeta's first true breakthrough in regaining his memory of Katniss and his love for her was when he heard her rendition of "The Hanging Tree". Lucy Gray not only stormed into the Capitol but she stormed into Peeta's muddled memory, and her music was higher than the hope Snow had brought down. Lucy Gray's song led Peeta and Panem into freedom, and it helped to restore Peeta as the hope and love of Katniss' life.

Lastly, the ending of the song greatly foreshadows Katniss' journey:

There's blood on the side of the mountain

It's turning a new shade of red

Yeah, sometimes the fire you founded

Don't burn the way you'd expect

Yeah, you thought that this was the end

Of course, we all know the end is far from over for Snow. As Lucy Gray told him once, "The Capitol show isn't over until the mockingjay sings". Katniss' story ends with her singing Lucy Gray's lullaby to her children. Katniss was the fire founded by Snow, and despite his best efforts, it didn't burn out or even burn the way he expected it to. The line "the fire you founded" is also perfect to describe Snow and Katniss' dynamic because in many ways Katniss was only a threat because Snow threatened her. It's the same dynamic as Harry and Voldemort's. If Voldemort had left Harry and his family well alone, Harry never would have been the perfect enemy to thwart him. In the same way, if Snow had left Katniss and her friends and family well alone, she may never have come for him the way she did or joined the rebellion.

Interestingly enough, if Prim's name had never been called not only would the rebellion most likely have been avoided, but Lucy Gray's music may very well have been truly forgotten. Assuming Katniss is the only one left who actually remembers the songs, we know from reading the books that Katniss does not like or want to sing simply because they are painful reminders of her father. If Prim had never been threatened and Katniss had never been a contender in the games she would have been subjected to a life of mining and may have let the songs fade from memory as she lived out her miserable slave life in District 12.

But as we know that's not what happens, and instead the memory of Lucy Gray and her music is forced from Snow as Katniss is forced onto this journey proving the memory of Lucy Gray is very much still alive except this time Snow can't catch her now.

Thank you for reading if you made it this far! Please share your thoughts if you'd like!

#the ballad of songbirds and snakes #the hunger games #suzanne collins #lucy gray baird #katniss everdeen #peeta mellark #coriolanus snow #maude ivory #mockingjay #olivia rodrigo #tbosas spoilers #tbosas

[variousqueerthings]

Daniel LaRusso: A Queer Feminine Fairytale Analysis Part Three of Three

(another massive, massive thank you to @mimsyaf​ )

part 1

part 2

8. Queerness and femininity and masculinity and the colour red and *record breaks*

If we spin the record aaalll the way back to this paragraph: “…looking at what it is girls and women in fairytales have/don’t have, what they want, and how they’re going to get it. It’s about power (lack of), sexuality (repressed, then liberated), and men.” Reading Daniel as a repressed, bisexual boy in a society that doesn’t accept his desires it’s interesting looking at how he moves through the world of the Miyagi-verse, at how threatened other men are by him, at how obsessed they are with him.

He’s out in the symbolic woods and these large boys and men see him and decide for whatever plot reasons to come for him. And they are large and violent and attractive and apart from Johnny again, they don’t have the nebulous excuse of fighting over a girl and even that excuse dies by around the midpoint when Johnny kisses Ali just to get a rise out of Daniel. He’s not trying to “win her back,” he’s not even really looking at her. He’s just trying to get a reaction. They don’t have any of the fighters in Rocky’s excuse either of Daniel being a macho opponent. 

You can read whatever subtext into TKK1 and TKK2 (which becomes especially tempting once CK confirmed that the guys he fought at seventeen have been thinking about him ever since – for thirty-five years), but TKK3 is where it’s really At in terms of obsession and lust and forbidden desires.

Silver is presented as both a handsome prince who saves Daniel and mentors him (where Miyagi is undoubtedly cast in a fatherhood role) and later on becomes twisted into a dark secret that Daniel has to keep, while he turns that thing that Daniel loves (karate, it’s… it’s karate… it’s also men, but it’s definitely karate, because karate makes him feel… things...) into an abusive, violent version of itself.

A wolf in sheep’s clothing.

But he’s also offering him something liberating. Whatever is going on in that nightclub scene is about something other than breaking Daniel down. Even the bloodied knuckles aren’t just about revenge. It’s about giving him something that he isn’t, in the end, willing to receive, at least not from Silver. In that roundabout, strange way of these feminine fairytales, it’s exploring hidden desires through the metaphor of karate.

Daniel wears red because it’s his colour. In the movies he wears red a lot. Often in scenes with violence in them (the beach/the hilltop in TKK1 and the date/the destruction of the dojo/the final fight in TKK2), but he also has a variety of shirts (and in TKK3 pants) that pop up all the way through the narrative. He wears a red jacket when he accepts Terry’s training, when he punches a guy in the face, and when he tries to get out of the training again (as badly as that goes).

Did anyone consciously think about red’s link to desire, obsession, and violence when they made these? Eh. But is it there symbolically? When he meets Johnny, when he fights Chozen, when he’s in emotionally fraught situations with Terry? Hell yeah.

Probably the most lust-and-violence infused red is that aforementioned punching-board-until-knuckles-bleed bit – not that I thought Terry was going to pull him in for a kiss, because I knew, logically, of course he wouldn’t right? There’s no way… is there? Or later on when Daniel punches that guy and ends up with blood all over his shirt and Terry once more grasps him, euphorically. Blood is violence. Blood is also desire. Red is Daniel’s colour, even though he doesn’t acknowledge it come Cobra Kai. (Maybe he just needs someone else - cough Johnny Lawrence cough - to inspire it in him again).

Daniel LaRusso’s narrative is exploring that most feminine of fairytale tropes: To want and be wanted by monsters and having to hide those desires.

“Maybe this time that strange churning in my stomach that feels like a mix of anticipation and fear will turn out good for me.” - Daniel’s mind.

At the end of the story, Daniel saves himself, with all of the strange mixed narratives around it, and the acknowledgement that the end of The Karate Kid Part Three isn’t satisfying and its aftermath will likely be delved into in the next season of Cobra Kai.

Nevertheless, he saves himself. Not from Silver or Kreese or Barnes, and not entirely, but he makes a decision not to give in to fear (and he continues to try and live by that decision, making it over and over again for the next thirty-five years, even when the return of Cobra Kai makes that difficult for him). 

He doesn’t do it by being the strongest in the land or even through a lucky shot (although that too). He does it by refusing to be like the male antagonists that surround him, by telling them they have no power over him. The narrative isn’t just his getting lost in the forest and all the monsters he finds there, it’s about how he redefines power for himself within that forest. 

He’s a man who isn’t violent, whose victories include helping out a girl whose ex-boyfriend just broke her radio, successfully doing the moves to a cultural dance he’s trying to learn, sitting with his father figure while he cries over the death of his own father, telling a girl that she’s just made her first friend, and breathing a sigh of relief that a tree that got broken has healed. 

Daniel LaRusso is a good boy is the point!

Karate is a metaphor. It can turn into many things: A series of lessons learned about how to be his own man and take care of his own house, a respect for the history of the father teaching him and sharing his home and story with him, fear, desire, masculinity (and the different forms that can take). 

When a tall, handsome stranger offers to teach him karate in the dark, without Daniel’s caretaker knowing how to help him, and twists that karate into something that hurts him - when he reclaims that, over and over, that means something too. 

This man is fine and definitely isn’t carrying the weight of buried karate-based queer trauma - could a traumatised man do this? *stares blankly at a former tormentor as blood runs down his forehead*

9. In Conclusion Daniel Has Kissed Dudes… Symbolically… But We Can HC Literally:

So there’s Daniel and his coded feminine fairytale narrative. It’s all a series of fun coincidences.

1. Ralph Macchio is just Like That

2. Red. All the red. 

3. large portion of his storyline is about lack of power. Yes, he regains that power by the end of the first and second movie through A Fight, but generally he is framed as powerless opposite these almost monstrously physically powerful boys/men. And in the third one it’s barely even about physical prowess (he’d still lose a real fight against Barnes or Silver) and more about regaining lost autonomy off the back of a manipulative, abusive relationship with an older guy.

4. The third movie in particular is narratively a mess, but if reimagined as a fairytale makes a lot of sense (because it’s secretly all about how karate is bisexuality and Daniel gets manipulated through that desire to be better at karate).

5. Queerness and femininity and themes about hidden desires that can only be approached sideways through couching those desires in symbolism: Handshake meme.

6. The fact that the more I think about it, the more feral I am for a Labyrinth AU.

7. To sum up over 5000 words of text: The inherent homoeroticism of wanting to be slammed against a locker by a bully, but extended over three movies and ever-more inventive ways of hurting pretty-boy-Daniel-LaRusso.

Johnny’s not going to be happy when he realises Daniel’s got other ex-rivals buried in his closet...

10. Some Other Stuff Aka The Laziest Referencing I’ll Ever Do

Further reading on trans Matrix

Further reading on masculinity and rape narrative in The Rape Of James Bond

Youtube Video from Pop Culture Detective (Sexual Assault Of Men Played For Laughs)

Some film/TV references in this: Dracula (Coppola), Princess Bride, Buffy The Vampire Slayer, Labyrinth, The Matrix, Rocky, Princess And The Frog, Cinderella, Enchanted, Shape Of Water, Swamp Thing, Phantom of the Opera 

Some fairytale references: Red Riding Hood, Cinderella, The Wolf And The Seven Little Kids, Alice in Wonderland, Wizard of Oz, Sleeping Beauty, Snow White, Beauty and the Beast, Company of Wolves (Angela Carter), Through the Looking Glass, Princess Bride

Also referenced is Alison Bechdel’s graphic novel and the subsequent musical Funhome. Further thoughts on this by @thehours2002​ and @jenpsaki​:

https://thehours2002.tumblr.com/post/650033577171533824/daniel-larusso-and-fun-home-click-to-enlarge

https://jenpsaki.tumblr.com/post/650530225997971456/cobra-kai-fun-home-inspired-by-goldstargirls

My list of Cobra Kai meta posts

I wanted to delve into fairytale movies more, but then I was like “fuck, I have actual work to do,” but I was interested in the ways male and female characters are written in these stories:

The Last Unicorn, The Never-Ending Story, The Dark Crystal, Legend, and Stardust.

The Last Unicorn is an interesting one because she’s not really human, until she is. It’s more like The Little Mermaid (the fairytale, not the Disney film) in tone, and of course there’s a pretty substantiated rumour that Andersen wrote that one as a metaphor for falling in love with another man (who eventually got married). 

Andersen in general is just fun to analyse as someone who popularized so many fairytales and exists as an ambiguously queer historical figure – might’ve been modern-day gay, bi, ace, but we’re just not sure. All your favourite fairytales can be read through the lens of queer loneliness and ostracization. Just like horror.

Anyway I didn’t go into the whole Little-Mermaid-Last-Unicorn transformation bit so much as the Monstrous-Desires bit, but I think there could be something to that too, with monsters representing otherhood and all. Stardust is a kinda-almost-this, except she sticks to her human form and all is okey-dokey by the end, she’s allowed to marry the handsome man and be a star.

The Never-Ending Story has Atreyu and Bastian and because of a lack of female characters, an interesting bond between the two of them, but mainly Atreyu is absolutely a go-gettem Hero Type and it’s just interesting to see how Bastian relates to him as both an audience insert, but also eventually as his own character in that world.

The Dark Crystal contains certain… androgynous elements of feminine and masculine coded characteristics in the main character because of how he’s not human, but also they do have a “female” version of his species that he needs to go save (and bring back to life) by the end, so in a way it’s both more and less heteronormative in its characters.

Legend sees another example of a monster (literally called Darkness and looking like a traditional devil) trying to seduce a princess through promises of power, and she “goes along with it” in order to trick him and succeeds in that trick, but is ultimately saved by the male lead. 

In conclusion: I don’t even have Shrek in this.

#daniel larusso #terry silver #the karate kid #the karate kid part three #cobra kai #ck #cobra kai meta #and in conclusion... brain brain brain

[harcourtholmesii]

A Strange Meeting

Fandoms: Dead by Daylight

Pairings: None

Warnings: - Reference to Violence - Referenced Gore - Referenced Death and Torture - Implied, Stated and Referenced Prejudice - Pretty Poorly Written

Words: 2019

I wrote this sometime ago, but I felt like posting some of my older works to Tumblr to get them out there. In this one, to clarify, I have this little headcanon that the Entity would want to get the most it can from its survivors and killers before tossing them into the void. So, the Entity forces some killers to be survivors and some survivors to be killers, so it might leech as much emotion, hope and fear out of them all.

Enjoy!

She had found a quiet place. It was hidden deep into the woods, far from the campfire’s warm glow, and out of sight of those judging looks. She could hear, carried along by the chilling wind, the faint cries of Dwight and Kate’s hollers as they searched for where she had hidden herself away. With her back pressed firmly to the chipping bark of the ulmus- elm behind her, she brought her knees up to her chin, muting her sobs. The cold wind swept through her, and beneath her long sleeves she could feel her hairs rising in horripilation.

 Goose-bumps. It was what everyone else called it. But why not use the scientific term? She didn’t understand. According to David, and everyone else probably, there was a lot she didn’t understand. Her father called it a ‘brilliant mind’, an ‘inquisitive mind’, but her mother referred to it in much the same way as everyone else. ‘Special’. ‘Unique’. ‘Unusual’.

 When the world around them began to collapse, everyone else ran to the door. When she was alone in the collapse, she just had to collect that one insect. Where one should run for a teammate, she had to collect the sap and take notes. She couldn’t help herself. That was what she knew; botany and entomology were her video games and childhood toys. She didn’t understand these trials. Never had she wished to be swept into a life or death game, and whilst other survivors lived for the chase, she despised having to run around. Her legs ached so much at the end of a trial, she would rarely wait to reach the campfire before collapsing to her knees. Even when those black, arachnid-like appendages tore her away from the safety of the fire, she could rarely find the strength to continue these trials any longer.

 Claudette’s head snapped up, hearing heavy footsteps approaching. It sounded much like David or Bill’s heavy boots; the last people she wanted to talk to. As she brought a hand up to the tree behind her, gaining some purchase on it so she might stand quickly and run, she was interrupted by the face of a man she had not met out in these woods. She had never run into another lost soul on her own before. She had always been by Dwight or the others, but now, she was caught out and unsure how to react.

 He was enormous. Like an ursus arctos horribilis- Like a grizzly bear in size, he was packed with muscle with wide grey eyes. He turned a dark gaze down to onto her; those grey eyes filled with mild curiosity. They carried a familiar weight to them, like the gazes she had seen many times when their group met survivors who had been there just as long as themselves (or perhaps longer). They were weary, exhausted and yet they looked at her with aroused suspicion. She noted the faintest dark stains on his clothes; there was blood, yes, like there always was, but a black powder mixed with mud and dirt caked the white of his collared shirt. He wore dark overalls with one strap snapped on the right side and, much like everyone else, his clothes were in such a disarray. How could a man like this be one of them? It was much like when she met David; just how could a man of his size, strength and temperament be a survivor?

 A crunch of leaves and twigs alerted her, Claudette’s eyes travelling up to the man’s face as he ducked down beneath a branch and with his back pressed to the tree, slid down to sit on her left side. He dropped heavily into the mix of dirt and roots, but kept quiet. She didn’t like this. She wanted to speak up and tell him to go away. This was her spot. But, instead all she felt was the urge to stand and return to the campfire.

 “Please stay.” Claudette hadn’t realised she had already started making a move to stand. His voice shocked her. It was a growl. Not like a threatening growl, but his voice was deep and broken that when his plica vocalis- vocal cords produced his words, it reminded her much like the deep bellows of a bear. She swallowed around a lump in her throat, feeling how her body tightened in fear. Her joints were strained, prepared for her to jump up and run like her body had never done so before. Even when she was in a trial, she had never felt so terrified. Nervously, she let herself slump back into her place at the base of the elm’s trunk. She was shaking.

 “W-Who…” She swallowed again, trying to gain the nerve to speak. “Who are you?”

 He turned his head to look at her; a slow, bored motion, with his grey eyes meeting hers. Even like this, he was still at least a foot taller. He was just… so… big…

 “Someone like you.”

 “H-How do you kno-?”

 “I guessed.” He interrupted her, turning his head away, his right hand brushing lightly at the dirt between them. She bit her lip to keep herself from yelling at him at how he was getting her jean pants dirty. What did it matter? They were dirtied from mud, blood and torn to shreds at the calf and knees. He glanced back up at her, one large finger beginning to scratch a pattern into the dirt. “Lost.”

 “W-What?”

 “You seem lost.” His eyes turned back to the dirt, glowering at a mistake he brushed away with his knuckles. His attention returned to dividing his gaze between her face and his picture.

 “W-Well, I’m not. I know where I can go and-”

 “It is not what I meant.” He said, stopping his digits from digging into the dirt. He turned his body, angling it towards her, a foot between them. He was uncomfortably close for her liking, but he didn’t push further. “Your mind seems elsewhere.”

 “And how do you know that?” She pulled her lips tight into a frown. She didn’t appreciate how he was analysing her. It was like how her mother tried to send her to a therapist, except instead of a sense of duty to her mother, she was kept there by her fear rooting her feet to the ground.

 “I know.” He hummed, returning to a relaxed position around the tree. “No one runs from the fire except for a few reasons. Since you are not screaming…” He trailed off, letting Claudette fill in the rest.

 “I… I just can’t deal with this any longer.” Well, he was certainly doing better than her therapist and actually getting her to spill something personal. Whether out of fear or not, it didn’t really matter. “I’m constantly afraid. I can’t keep up with this. I just… I just want to go home.” The world around her grew blurry, her eyes beginning to sting as tears welled up and then rolled tracks down her hot cheeks.

 He didn’t speak. He had stopped drawing in the dirt, and kept his eyes trained on her and how she rose her hands up in fists to wipe away the tears. “I just want to go home to my parents. To my microscope and studies. I want to go back to college. If anything, people whispering behind my back is nothing compared to a hook going through it.” She bawled, bringing her body into a curled position.

 “What is a m-micro-… ma-icro-scopp?” Her wide eyes turned to look up at him, surprised to find him tilting his head like a giant dog. He was curious, and the thought that this man didn’t know what a microscope was… It was a welcome distraction.

 “A-…” She wiped the tears from her eyes, trying to gather herself. “A microscope i-is a tool used to analyse samples. Like being able to see… Um…” She reached down to the grass and dirt, pulling up into view a single leaf, crumpled, but otherwise intact. “Inside a plant there are cells. By having a sample like this leaf under a microscope, you can see them.”

 “How?” His growl of a voice caused her body to shudder. Despite her discomfort, his being there as a stranger just listening to what she had to say reminded her of how someone would message the forums asking a simple question she could answer. At least over the internet and in the college chatrooms, people appreciated her knowledge.

 She expanded on how it all worked, and felt herself go on and ramble. What could have been answered in fifty words had ended up becoming an entire thesis. Then came the questions about how she got into college studying science as a woman and what the internet was. Like Ashley and Laurie, it seemed he had been ripped out of a time long before her own. How long had he been here? Still, who knows how much time passed, but through it all, whilst he sketched into the forest floor, she answered all of his inquiries and explained how it all worked. She appreciated how he didn’t seem to have any prejudices despite his time, and when bringing up the topic, he simply shrugged his shoulders.

 “It never mattered to my father. It doesn’t matter to me.”

 When Claudette felt her rump and tailbone beginning to ache, she stood slowly, feeling a little better to talk to someone other than her teammates. As she stood, so did he; carefully sidestepping around his sketch until he faced her. She felt a little trapped just due to his sheer size and might, but when she moved, he did not reach out or follow behind. Instead, he took a step back in the opposite direction.

 “Come with me.” She said, feeling a flush enter her cheeks. It was a little embarrassing saying that so quickly, but after their hours (she had to presume) of talking, she didn’t want to return to the group without him. Who knows? A man of his size might be able to help them in the trials.

 “No.”

 “W-Why not?” She felt a little astounded. Why wouldn’t he want to come? “I-It is okay. No one is going to run you off. I just needed time to myself. You should come with me. I’m sure the others will be happy to meet you.”

 “No. I have my own to return to.”

 “There are other campfires?” He looked over his shoulder, back through the thick woods from whence he came.

 “Hundreds.”

 “W-What?”

 “Hundreds, scattered all about. We can’t go very far, but you are not the first person I have met out here.” He stepped away from her, the shadows over his form hiding his face from sight. The moonlight streaked that streaked through the woods refused to move and just grant her one last look at him. “I have to return to my own. In time, may we meet like this again.”

 “Wait!” But already, he had vanished back into the dark. How a man like that could move so quickly and quietly, she had no clue. But apart from his patch of dirt, there was no sign he had even been there. In the dirt, what she saw drawn there was a truly nice sketch, if a little primitive due to the lack of tools. It was her face. Her face was in the dirt, with a small smile on her face. She bit back a huff of laughter- not out of actual amusement, but out of sheer irony that he would predict the outcome of their conversation.

 She turned on her heel and went back the way she came, noting the carvings of Mashtyx in the bark of the trees, reminding her of her path. Now, as she returned to the safety of Kate’s lullaby and the warm glow of the campfire, she came to realise what was stained on his clothes. What gave him such an earthy smell. It was coal dust, much like what she smelt in the coal mines of the Macmillan estate.

#DBD #Dead by Daylight #The Trapper #Claudette Morel #DBD Fanfiction #DBD Fanfic #HarcourtHolmesII

[gaystardust]

Twelve Weak Lies 1/? [Kanera Week Day 2]

Synopsis: An injury forces Hera and Kanan to land on Eso, a planet neither of them have heard of. Neither of them expect to fall undercover as a young couple expecting the first child, just because the people of the village are so convinced that’s who they are. Rating: Teen and Up Warnings: Discussion of pregnancy and pregnancy loss, although neither of those actually happen. Some discussion of poverty and existence. AO3 Link: [link] A/N: There is a host of made up fruit/vegetables (or our-world food given a slightly warped version of its name), some made of Twi’leki culture. This is Chapter 1, and the rest of the chapters will go on AO3 at some point after Kanera Week because I am waaayyy behind.

 ----------------

 “What about that one?” Kanan asked, making a stab at something on the star map Hera couldn’t even see.

She leant forward, squinting at the… planet? Moon? Speck of dust caught in the projector? “I’m not sure I even know where that is.”

He shrugged. “Doesn’t matter where it is. I’ve got a feeling.”

A feeling. It was always ‘a feeling’, a phrase Hera couldn’t parse well enough to know when it was just a hunch, or something a little more concrete.

Of course, concrete generally meant something mysterious and borderline magic that she would never be able to understand and Kanan would never explain.

Hera sighed, turning back to the ground slowly rising towards them. It was a powdery brown colour, muted across the whole of the planet bar thick splotches of green and blue in a band around the equator. A quick scan showed few life forms, unsurprising based on the size of the planet, centred around several obvious sentiments.

She’d spent a good few hours in hyperspace trying to work out what exactly this planet was and if there was any infrastructure they should be aware of. But it was still just a blank pinprick in the middle of nothingness, with absolutely no information past a name and an export listed anywhere on the holonet.

Eso. Main export, something helpfully named the Eesu fruit, and wood from something called the Uko tree.

So basically there was nothing. Absolutely nothing. And yet, Kanan wanted them to go there, so they were going.

Or maybe Hera was just tired of arguing. They’d had nothing but fights the last few days, the stress of their repeated failed missions rubbing them both the wrong way over and over.

The wound on her side still stung, and her leg wouldn’t hold her full weight unless she was leaning on something.

It had been an eventful tenday, to say the least.

The atmosphere seemed pretty good, rocking the ship a small amount as they entered. The gravity felt… fairly average, which made sense for the size of the planet.

Hera tapped the intercom button on the control panel, leaning forward. “Almost ready to land, Kanan. If you could come up, that would be great.”

It would take a little while for them to reach the ground, but having them both up front would help. There was so little they could do without the pretence of a fully functioning crew - or even a family unit, in some cases. It stopped people questioning how two people so young had ended up with a ship of their own, travelling the galaxy instead of enlisting or settling down to start a family.

There were no prizes for guessing which comment was directed at who.

By the time the landing legs of the Ghost had fully extended, Kanan was finally in the cockpit. Whatever he’d been doing in his quarters had apparently held him up, but nothing past the “sorry, I was busy” indicated what exactly that was.

A dark-skinned Kiffar waved them down, her dark hair tied up and fluttering behind her back. Hera could already see the pouch on her belt, likely heavy with credits and whatever else people were paying in.

Kanan sighed, standing without prompting. Sometimes, his Force sensitivity came in handy. “I’ll go get it.”

The minute they were securely landed and shut down, the Kiffar was stepping up to meet them. She was tall, as tall as Kanan if not more so. She looked somewhat strict, but nothing they couldn’t handle.

“Y’new here?” she asked, voice rough under her Outer Rim accent. Kanan nodded, immediately stepping up to his usual role. They’d practised this too many times to slip up. “Yes, Ma’am. Looking for somewhere safe to dock our ship, likely long term.”

“You planning to stick around?” “We hope so,” Her added, moving to stand slightly behind Kanan’s shoulder. “We could do with a fresh start.”

For a second, the Kiffar considered them. “Work?”

Kanan nodded to himself. “For me, not for her,” he shrugged towards Hera. The Kiffar gave her an odd look, analysing something between them that was a little uncomfortable. “She’s not well at the minute.”

It wasn’t a lie, and it wasn’t hard for her to see. Hera knew she was washed out, skin grey-ish as she recovered from the obnoxious injury on her side. Even when she tried to stand straight, she was hunched to one side.

Whatever the Kiffar was looking for, she clearly found. “Well, if you’re sticking around, call me Mihra. Now…” She glanced to the Ghost, the cogs ticking behind her eyes. “Y’planning to stay on the ship, or are ya looking for a house?”

They looked between each other, considering for half a second. “Yeah,” Kanan spoke up, turning back to Mihra. “Yeah, somewhere to live would be great.”

She nodded, turning to call something over her shoulder. It sounded like Huttese, but it might not be at all - definitely derivative though. One of the attendants rushed off somewhere.

“Tiss will see what she can find. Let’s get your ship settled, and then we’ll get you somewhere to stay.” She must have noticed the confusion in Hera’s face, unsure why they were willing to help. “Relax. You aren’t the first people to turn up like this, and y’won’t be the last. We’re a community of people on the run from something or other. If you’re willing to work, we’re willing to help.”

She said it with finality, as if she expected neither of them to ask any more questions.

And so they didn’t.

—

They were barely settled an hour when someone knocked on the door. The wood rattled its metal fixtures, a noise neither of them were particularly used to, before opening easily.

Hera reached for her blaster automatically, Kanan doing the same. She already had it trained on the doorway when a tall, Rodian woman stepped forward. Her eyes trained on the blasters for a moment, but she didn’t react.

Her bright, star-field eyes watched the two of them before she half turned towards the doorway. “Maar? I would stay outside a little longer, if I were you.”

Hera half-lowered the blaster, but not completely.

When the Rodian turned back, she smiled. “Apologies, I clearly should have waited. Mahra told me you were looking for work?”

Kanan stepped forward, nodding. “I am.”

She nodded. “The name’s Tsiin, I work in one of the forests just out of town - fruit picking. Not necessarily difficult, but we mainly pick for ourselves so it’s fairly rewarding.”

There was a moment of quiet while Kanan considered it. “And the pay?”

“I should have guessed,” Tsiin laughed, shaking her head. “It’s fair. Some of the payment is in food, we pick a variety so it doesn’t get too bad. Otherwise, it’s pay based on quantity. 30 credits per bag, we normally get a handful done between us a day.”

Kanan clicked his ton. “So, why do I pick that instead of the other options?”

“Three things,” she held her hand up, four fingers curled up into a fist. “Safety. Shorter hours. Less Imperial pressure.” Something about that made Tsiin look directly at Hera. “More time at home, supporting your partner here.”

“That’s four things,” Hera pointed out sharply, but Kanan half spoke over her.

“Fine. When do I start?”

“Day after tomorrow. Get yourselves settled tomorrow. I’ll come and collect you an hour after dawn, and you’ll be back before sundown.”

To Hera, they sounded long hours of physical labour, but Kanan seemed to think it was fine. “Okay. Deal. I can’t promise how long we’ll hang around, though.”

“Oh, we’re used to that.”

The new voice was deeper, raspier, strangely melodic for the near-human body it came from. Their hair was so dark, it stuck out against the almost white colour of his skin and their clouded eyes. In one hand they held a bag of interlocking ropes, metal containers swinging low.

The cane in their other hand tapped on the floor just in front of him.

They smiled vaguely into the room, mostly looking towards them but not perfectly. “Apologies for interrupting, but we are more than used to people coming and going in our community. Fast friends are common here.”

Tsiin sighed from where she should. “This is Maar. They run one of the market stalls in town.”

Kanan stayed quiet, but Hera forced herself to smile, hoping he could hear it in how she spoke. “Nice to meet you, Maar.”

“Likewise,” they replied, before holding out the rope bag towards her. It was more direct than previously, her voice helping them pinpoint a little more accurately. “I brought you some necessities to last you until at least tomorrow. Hopefully, you will find them helpful. There is not much, but I checked with some locals for what you would need to eat, Miss…”

“Hera,” she supplied quickly, hoping against all odds that this wasn’t a bad idea.

“Miss Hera. I asked what you could eat, while Kanan, I can guess myself,” they smile was a little too wide, but not threatening in its strangeness. “Hopefully you will find something to your liking.”

“There’s bedding in here as well,” Tsiin said carefully. “And spare clothes. Like I’m sure Mihra said, we are more than used to strangers arriving on our doorstep.”

Hera stepped forward again, letting Kanan take his time. Whatever he was reading into the two, it was taking all of his focus. “Thank you, honestly. It’s incredibly generous of you.” She stepped forward to take the bag, surprised by how heavy it was, and how little effort Maar had been exerting.

Tsiin looked between both of them, before nodding .”Alright, well. We’ll let you get settled. Come on, old man, I’ll walk you home.”

The near-human turned sharply, putting their arm out to take Tsiin’s. “You two had best come and visit me soon.”

Now, Kanan replied. “Of course, Sir. As soon as we can.”

“And I shall hold you to that!” Something in their tone was completely serious.

Just as they crossed the threshold, Tsiin twisted back. “Day after tomorrow, kid. Me and the crew will come and get you.”

Kanan nodded, giving her his best grin. “I’m looking forward to it.”

—

The house was small and dark, thankfully cool despite the high humidity outside. It almost reminded Hera of the buried houses on Ryloth, chasing away the burning sun and the impending dessert by blocking out the sunlight that most humanoids were desperate for.

Kanan, she knew, hated it. He’d told her often enough in the week they’d been based on Eso. Something about the lack of natural light, and the strange feeling of being half-buried in clay and hay walls.

Still, Hera had forced him to stick with it, pushing through the wobbliness in her right thigh as she ran more whitewash across the peeling wall. One of the neighbours had given it to them, with instructions to paint their new home before the peeling paint cracked through to the wall and the smell of dung crept from its prison.

She hated painting, had since she was a child, but even Hera had to admit there was something nice about painting walls. There was no precision to it, covering such a large space in a single colour, but she could still see where she’d succeeded to cover it.

It was more than a little satisfying.

The twinge in her torn rib muscle reminded her not to lift her arms too high, but there was no way she could balance on one of the stools they’d found. The wound in her leg was healing quickly, but it had been a nasty shot, with metal hooks digging into her leg before she’d had a chance to think. Even now, weeks later, it twitched and ached whenever she put weight through it.

Taking a deep breath, she hobbled over to the mats they’d been using as a bed (two of them, layered over each other for some comfort, even if it meant sleeping next to each other). The drop to the ground was further than she would have liked, her leg giving out halfway as she hit the mat with a thump.

At least, she mostly made it onto the mat - before decided that was far enough. The mats themselves weren’t thick enough to hold her at an uncomfortable angle, even half hanging off them, and moving much further would take more energy than she had. Pausing there would work which better

Hera reached for the comm tucked into the waistband of the shorts she’d claimed from Kanan (she tried not to think of them as underwear, because they weren’t - he just slept in them). They were easier to move in while she renovated the space they would be living in for the next few weeks at least.

“Kanan?” she asked carefully, listening for the telltale bzzzzz-click to say she’d connected. “Make sure to get eggs for tonight as well.”

A laugh came from the other end, covered in static but completely Kanan. “I know, Hera, I’ve already got them.”

“And the vegetables I asked you to get?”

—

Kanan stared down at the pile he was carrying. “Okuru, gulalung, solum and greens.”

“And the annuum?” Hera added, the lecture obvious in her tone.

“Of course,” he lied through his teeth, doing his best to sound exasperated. “How could I forget?”

The sigh on the other end of the line made the comm crackle unpleasantly. “Just remember to pick them up before you come home. And the persipan. Can’t make sweet curry without them.”

“I know, I know, you tell me often enough.” He absolutely did not know, they had never discussed this, but there was something about admitting that that felt like it would blow their cover. After all, the people of this community had decided they were an established couple, and he wasn’t about to make them question that.

He could hear Hera moving around on the pallet they’d been sleeping. It creaked if you moved to hard. “Can you get some alata as well? I fancy some. We can cook them with porridge in the morning.”

It had been way too long since they’d eaten fresh fruit and vegetables instead of the rations they mainly ate while travelling, and the first time they’d had a steady income in even longer. He was already heading back to the market anyway, so he might as well.

That didn’t stop him filling his voice with exasperation, the fake tone he used for chores he would do without her even asking. “Of course, your highness, I’ll get you what you want.”

A hand reached out, catching him around the back of his head. “Wha- hey!” He spun around, face to face with a fairly old Nagai, who had already caught him at their market stall that morning. “Maar?”

The comm in his hand crackled. “Kanan?” Hera’s voice was filled with concern.

Maar’s eyes were greyed over with cataracts, but they still landed on the comm. “Apologies, I have interrupted you. Continue.”

Kanan watched them carefully, not sure how to take this interruption. Still, he returned the com to in front of his mouth. “Don’t worry, Hera. It’s just a friend being a pain-” Beside him, Maar laughed as if that was the best joke they’d ever heard. “I’ll be back soon. I’ll speak to you later.”

“You’d better, Jarrus, or you’ll regret it.” The comm pinged once more as the connection was severed. Immediately, he spun to the Nagai.

“Really?”

“You shouldn’t complain about your partner, Kanan Jarrus. She is trying her hardest to keep her spirits up while shut inside.”

Of course, Maar knew nothing about why Hera was shut inside, past that she was unwell. That was a lie they kept spreading - not that it was completely a lie, just an oversimplification.

“I know, Maar,” Kanan said with a shake of his head. He knew she was struggling, but what else could they do. She could barely walk, never mind anything else. “She’s trying to keep herself busy, painting the house while I’m at work, but- OW!”

This tap on his shoulders was barely a tap. “Kanan Jarrus! You should be more careful. Your wife-”

“Partner,” he shot in quickly, trying to stop that instantly.

Maar nodded almost immediately. “Your partner is in a delicate position.”

Something clicked in Kanan’s mind, that perhaps telling everyone Hera was unwell would end badly for them both, long term. “No, no, she’s not that kind of sick. She’s just making sure to give herself plenty of time to rest.” Then he laughed, brushing his hair back out of his eyes. “Besides, I’m not sure I could get her to stop if I tried.”

The Nagai seemed to consider this, before nodding again. “Still. She should not exert herself, no matter how tempting.”

“Try telling that to Hera,” Kanan continued to laugh.

They arrived back at Maar’s stall, which they had left completely unguarded while they wandered off to… hopefully do something other than following Kanan, but he honestly couldn’t be so sure.

Each of the shelves and crates was practically overgrowing with local fruit and foraged roots, along with cheaply imported items. The whole place smelled like overheated sweetness, with a side of acidity.

Maar didn’t say anything as they placed food items into Kanan’s arms, and the robe bag they had given them on the first day that had arrived there.

“No, Maar, we don’t need any meiloorun you’re fine, or any eesu, I’ll be getting some from work later this week, I don’t have the money to-”

Maar shook their head. “Take them, from me to you. I have seen many like your partner here, and fresh food is always good. After all, she will need her strength, and you will need to be prepared. The next generation are always particular in their wants.”

Kanan blinked. “I’m sorry, what?”

Maar watched him carefully. A few skinny black braids falling in front of their eyes before they spoke again. “Apologies. I assumed you already knew - it has been the talk of the town for the last few days.”

Thoughts swirled in the back of Kanan’s head. Yeah, okay, the town wasn’t that big - maybe a few hundred people maximum, and most of them lived identical lives to all of the other’s around them, so gossip was a bit of a thing.

They were meant to be laying low.

“That… Hera is pregnant?”

Maar nodded as if that was obvious. “Of course. That is why she is in seclusion, yes? Not a practice I would use myself, but I know many species - including Twi’leks, she is a Twi’lek, yes? - use them as standard practice.”

The townspeople thought Hera was pregnant. They had taken the fake relationship between the two alongside Hera’s ‘illness’, and decided that meant they were going to be parents.

It took a few seconds, and a few deep breaths, for Kanan to pull his thoughts together. “No, no, she’s… she’s not in seclusion, Maar, she’s just more comfortable at home.” Maar gave him a look of complete disbelief, and Kanan made himself press on. “She has an injury to one of her legs that’s playing up.”

They nodded as if a grave secret had finally been explained. “Well, I’m sure she’ll be up and walking in no time. Just make sure you get her the food she has requested - the body knows what it needs better than we could ever understand.”

All Kanan could do was nod. If Maar wasn’t going to understand that Hera wasn’t pregnant, he wasn’t going to push it. Stars, he couldn’t even make himself think about it fully.

He handed over the credits to pay, Maar handing him back the chunk that would have covered the extra fruit. Kanan didn’t bother arguing, quickly taking his leave and ending their conversation abruptly. The Nagai wasn’t at all putout.

It was only when he was sure they were out of earshot that Kanan felt safe to react. “Shit.” At least Maar had covered Hera’s requests - he wouldn’t have to face anyone else that day. “Shit.”

—

“You didn’t tell him I wasn’t?”

Hera stood with her hands on her hips, poised like she was ready to fight. She had him quite literally cornered, standing in the centre of the room while he was sat on the bed.

“I told him you weren’t secluded, I just didn’t specifically say you weren’t pregnant.”

It had seemed logical at the time to let the cover the town had invented for them fly, but Hera was taking this much worse than Kanan had thought she would. He’d assumed that he would come home, explain what had been said, and they would laugh about it - but, no, Hera was taking personal offence to it.

She threw her hands in the air, lekku barely bobbing behind her. “That’s the problem! You should have just said I wasn’t, corrected him before it went any further.”

Kanan shrugged. He wanted nothing more than to pull his knees up to his chest, but he’d been trained better than that. “Does it really matter? They’ll work it out soon enough, once you’re well enough to wander around and they realise you clearly aren’t pregnant.”

“They’re just going to assume I’m not showing yet,” Hera added almost too quickly. “Or worse.” Her voice cracked as she said that. Then she shifted, dropping onto the mat beside him. Her head fell into her hands. The defeat in her voice was obvious “I don’t want to be involved with this, Kanan.”

Kanan shifted himself to put his hand on her shoulder. “I’m sorry, Hera. I didn’t think about the impact this would have - to be honest, I’m not sure I even thought. I was so caught off guard, I’m not sure I was planning anything.”

Hera made herself breath, lekku still unnaturally calm. “I know.” She sighed, pushing herself further forward. “I know you didn’t mean it, I just… I don’t know how to deal with that.” There was another pause before she let out a frustrated, grumbling sound. “But I don’t know how we get out of it without sounding awful, especially with people giving us free things because they think I’m pregnant!”

He nodded, looking towards her carefully. “We could always let them have their rumour. Leave before they realise you’re not showing because you’re not actually pregnant.”

“That’s a terrible idea,” Hera said quickly. “We’d be breaking their trust, making them think something about us that we’re not. It’s one thing to keep saying ‘partners’, it’s another to actually commit to that.”

“But you’re considering it.”

“Yes. Yes, I am.”

Everything dropped away for a moment, except the bubbling of the curry over the fire. It smelt deliciously spicy and sweet, filling the room around them.

For the first time in a long time, Kanan wondered if he should meditate.

He immediately pushed that out of his brain.

Hera sat up a little straighter. “ Can humans and twi’leks even have children together?”

“I mean, yeah, of course they can?” Kanan couldn’t help the question in his voice, wanting to know how he knew that when she didn’t. “I’ve never met any properly, but I saw plenty of blended families when I was growing up.”

Her body stopped moving, even though it barely was in the first place. For a single breath, she was just looking at him, and he was looking at her, and something around them shifted.

“I’m never letting you near me again,” she said finally.

“What?” Kanan huffed, gesturing towards her where she stood in front of him. “I’ve never even been near you! At all! What- I- You-” He took a deep breath, staring towards the shuttered windows. Hopefully, no one could hear them. “Look. If it bothers you that much, I’ll start correcting people.”

Hera sighed, flopping back on the mat as if she’d given up. She tried to hold back the wince, but Kanan still noticed. He didn’t comment.

“I can’t believe I’m saying this, but you shouldn’t. Start correcting people, I mean.” She didn’t look completely convinced, but something must have done it. “It might be easier if they think I’m staying at home because of morning sickness. We wouldn’t have to explain why it’s taking so long to recover from a minor injury.”

“Do you think we can pull it off, though? No offence, but we’ve known each other for - what, a year? Do we know each other well enough to pass for a couple who would be having a kid together?”

Hera shrugged where she lay. “I don’t think it matters, Kay. Not every couple is physically affectionate in public - some aren’t affectionate at all. Hopefully no one will notice.” Then she laughed, bright and filling the room. “Besides, we wouldn’t be fake dating, we’re just… not correcting their assumptions. It doesn’t mean we have to actually pretend we’re together.”

Kanan hummed to himself, trying to find the line in his head. It would be incredibly difficult to find the line where people would just accept their relationship, and not ask any questions.

“So we have… what? Two, two and a half months here then?”

Hera nodded. “That would be the plan. We can probably go for more if we need to, but let’s aim for that.”

Two and a half months. Just two and a half months.

He could probably manage that.

#kaneraweek2020 #kanera #star wars rebels #hera syndulla #kanan jarrus #and a whole host of ocs! #boy this one got away from me #there will be more chapters i promise! #my fic #zi writes #zi speaks

[sbrocks99]

Running a Random Forest

Hey guy’s welcome back in previous blog we have seen that, how to run classification trees in python you can check it here. In this blog you are going to learn how to run Random Forest using python.

So now let's see how to generate a random forest with Python. Again, I'm going to use the Wave One, Add Health Survey that I have data managed for the purpose of growing decision trees. You'll recall that there are several variables. Again, we'll define the response or target variable, regular smoking, based on answers to the question, have you ever smoked cigarettes regularly? That is, at least one cigarette every day for 30 days. 

The candidate explanatory variables include gender, race, alcohol, marijuana, cocaine, or inhalant use. Availability of cigarettes in the home, whether or not either parent was on public assistance, any experience with being expelled from school, age, alcohol problems, deviance, violence, depression, self esteem, parental presence, activities with parents family and school connectedness and grade point average. 

Much of the code that we'll write for our random forest will be quite similar to the code we had written for individual decision trees.

First there are a number of libraries that we need to call in, including features from the sklearn library.

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

Next I'm going to use the change working directory function from the OS library to indicate where my data set is located.

os.chdir("C:\TREES")

Next I'll load my data set called tree_addhealth.csv. because decision tree analyses cannot handle any NAs in our data set, my next step is to create a clean data frame that drops all NAs. Setting the new data frame called data_clean I can now take a look at various characteristics of my data, by using the D types and describe functions to examine data types and summary statistics.

#Load the dataset

AH_data = pd.read_csv("tree_addhealth.csv") data_clean = AH_data.dropna()

data_clean.dtypes data_clean.describe()

Next I set my explanatory and response, or target variables, and then include the train test split function for predictors and target. And set the size ratio to 60% for the training sample, and 40% for the test sample by indicating test_size=.4.

#Split into training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']]

targets = data_clean.TREG1

Here I request the shape of these predictor and target and training test samples.

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

From sklearn.ensamble I import the RandomForestClassifier

#Build model on training data from sklearn.ensemble import RandomForestClassifier

Now that training and test data sets have already been created, we'll initialize the random forest classifier from SK Learn and indicate n_estimators=25. n_estimators are the number of trees you would build with the random forest algorithm.

classifier=RandomForestClassifier(n_estimators=25)

Next I actually fit the model with the classifier.fit function which we passed the training predictors and training targets too.

classifier=classifier.fit(pred_train,tar_train)

Then, we go unto the prediction on the testator set. And we could also similar to decision tree code as for the confusion matrix and accuracy scores.

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

For the confusion matrix, we see the true negatives and true positives on the diagonal. And the 207 and the 82 represent the false negatives and false positives, respectively. Notice that the overall accuracy for the forest is 0.84. So 84% of the individuals were classified correctly, as regular smokers, or not regular smokers.

Given that we don't interpret individual trees in a random forest, the most helpful information to be gotten from a forest is arguably the measured importance for each explanatory variable. Also called the features. Based on how many votes or splits each has produced in the 25 tree ensemble. To generate importance scores, we initialize the extra tree classifier, and then fit a model. Finally, we ask Python to print the feature importance scores calculated from the forest of trees that we've grown.

# fit an Extra Trees model to the data model = ExtraTreesClassifier() model.fit(pred_train,tar_train) # display the relative importance of each attribute print(model.feature_importances_)

The variables are listed in the order they've been named earlier in the code. Starting with gender, called BIO_SEX, and ending with parental presence. As we can see the variables with the highest important score at 0.13 is marijuana use. And the variable with the lowest important score is Asian ethnicity at .006.

As you will recall, the correct classification rate for the random forest was 84%. So were 25 trees actually needed to get this correct rate of classification? To determine what growing larger number of trees has brought us in terms of correct classification. We're going to use code that builds for us different numbers of trees, from one to 25, and provides the correct classification rate for each. This code will build for us random forest classifier from one to 25, and then finding the accuracy score for each of those trees from one to 25, and storing it in an array. This will give me 25 different accuracy values. And we'll plot them as the number of trees increase.

""" Running a different number of trees and see the effect of that on the accuracy of the prediction """

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)):   classifier=RandomForestClassifier(n_estimators=idx + 1)   classifier=classifier.fit(pred_train,tar_train)   predictions=classifier.predict(pred_test)   accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

As you can see, with only one tree the accuracy is about 83%, and it climbs to only about 84% with successive trees that are grown giving us some confidence that it may be perfectly appropriate to interpret a single decision tree for this data. Given that it's accuracy is quite near that of successive trees in the forest. 

To summarize, like decision trees, random forests are a type of data mining algorithm that can select from among a large number of variables. Those that are most important in determining the target or response variable to be explained.

Also light decision trees. The target variable in a random forest can be categorical or quantitative. And the group of explanatory variables or features can be categorical and quantitative in any combination. Unlike decision trees however, the results of random forests often generalize well to new data. 

Since the strongest signals are able to emerge through the growing of many trees. Further, small changes in the data do not impact the results of a random forest. In my opinion, the main weakness of random forests is simply that the results are less satisfying, since no trees are actually interpreted. Instead, the forest of trees is used to rank the importance of variables in predicting the target.

Thus we get a sense of the most important predictive variables but not necessarily their relationships to one another.

Complete Code

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

os.chdir("C:\TREES")

#Load the dataset

AH_data = pd.read_csv("tree_addhealth.csv") data_clean = AH_data.dropna()

data_clean.dtypes data_clean.describe()

#Split into training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']]

targets = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test  = train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#Build model on training data from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train)

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

# fit an Extra Trees model to the data model = ExtraTreesClassifier() model.fit(pred_train,tar_train) # display the relative importance of each attribute print(model.feature_importances_)

""" Running a different number of trees and see the effect of that on the accuracy of the prediction """

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)):   classifier=RandomForestClassifier(n_estimators=idx + 1)   classifier=classifier.fit(pred_train,tar_train)   predictions=classifier.predict(pred_test)   accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

If you are still here, I appreciate that, and see you guy’s next time. ✌️

[appears]

Has it been two weeks or two years? Notes on Taylor Swift's Lover

I got swept up in all of the hype of the new Taylor Swift album, reading retrospectives and reviews and analyses and theories, and listening and re-listening to what is basically another carefully crafted season of the Taylor Swift show. It was both over- and underwhelming, and though the album has been out two weeks now, it feels so much longer.

What Swift is really great at is good old-fashioned song-writing, the type with easy and instantly recognizable hooks and personalities, such that a listen or two is all it takes to get to know a song. I have listened to my favorite tracks a dozen times at least and the entire album straight through four times and it already feels as comfortable and familiar as an old sweater; this speaks to both an achievement of an album that contains eighteen songs and a failure of its level of sophistication. But no matter how catchy an album is, once the trail of breadcrumbs left in the form of puzzles and clues and hints and references and Easter eggs have been eagerly plucked, gobbled up and spat out solved, some of the excitement and almost all of the novelty disappears. Something gets lost. Something new takes its place. Longevity will vary according to fan-level, but as Swift herself once said three albums ago, I'm like, I just, I mean this is exhausting, you know.

This album cycle was not unlike the fatigue that follows binge-watching, like waiting a year and a half for your favorite sci-fi homage to return only to greedily consume all eight episodes in a nine-hour fog of sensory overload, blearily stumbling away from the screen to wonder if having instant access was a Pyrrhic victory, not because the show itself wasn't entertaining, but because it seems somehow a little sad and cheap, that something that could take so much time and care to put together, and generates so much excitement, can be consumed in a few hours and forgotten in a few days. What a banquet we can all feast upon at once! but reviews of Orange is the New Black's last season are already buried somewhere in the archives. Hey, people want they want, and they usually want it all right now; the fact that human beings are typically very bad at understanding what they actually want is irrelevant to the current pace of pop culture. If you're dizzy, the only solution is to get off the ride, keeping in mind you've exiled yourself from the conversation -- sorry, it’s been almost four months, no one cares what you think about Game of Thrones’s last episode anymore. It's no wonder Netflix is experimenting with the traditional model of staggering episodes on a weekly schedule rather than dropping them all at once: all the ice cream and pool inflatables are no match for the emotional and physical capital that can be generated from a sustained, long-term hype machine with weekly beats, like pulse rates that spike sales and interest intermittently, rather than once, all too briefly.

But this is where we live now, and Swift either shrewdly or stupidly tapped into the phenomenon, having us all spend the majority of the album's peak in the inception and pre-order phase, nudged with the help of a de rigeur Japanese model of releasing various editions with slightly different content to scoop extra sales during the album's limited maximum-sale window: the first week ("[b]ut it also leads to a front-loaded first week, as Lover burned off much of its sales once the pre-order period ended. Fans will certainly continue to buy Lover in big-box retail stores and on her website, but we’re not going to see the same astronomical traditional album sales week after week"  -Forbes). And like the end of all major events we spend so long awaiting, we now just live in its memory, a little burned out.

I grow weary with Lover. I find it interesting, a sort of pop-music quilt of patchy trends and too-little, too-late band-wagon jumping like “You Need to Calm Down,” and “The Man” two songs that have frustratingly great hooks (and wow “Miss Americana & The Heartbreak Prince” which I initially loved because it sounded like a lost track off of Born to Die, but which I'm starting to resent for the same reason), a couple of surprisingly nice old-Taylors (“Lover,” “Daylight”) a few hints of interesting new-Taylors (“False God,” “It's Nice to Have a Friend”) and filler “content” that is far too specific to be universal in any way that I now mostly skip over (you know the ones, and also the ones that sound too much like stuff off of Reputation). I've never been one to listen to lyrics over the actual music, but Swift makes it impossible not to, and I find myself so lost in her head that I can't really enjoy the forest for the trees.

I now find myself enjoying Reputation a lot more. It feels unfair to compare it to Lover, with its Max-Martin-and-Shellback-pedigreed production, and I certainly don't want to turn this into a case of Antonoff versus the traditional Swedish school of pop, but Reputation’s theme feels more cohesive and confident as a whole, and somehow more honest in its spite. The music is also better. Anyway, don't ask me, I'm a 1989 girl. Everyone reacted to it and everything we missed is in a YouTube video uploaded last week, and up next, here are some plan with me Taylor bujo spreads and I listened to every Taylor song for the first time and a mukbang set to Taylor songs, probably, but only for a few days more before it all disappears into the Internet’s deep drawers because the ride is already over now with too much to keep us busy until TS8, and I'm happy to get out of this only $12.99 poorer.

#pop #music #taylor swift #lover #ts7

[sciencespies]

Scientists unravel how and why Amazon trees die

https://sciencespies.com/nature/scientists-unravel-how-and-why-amazon-trees-die/

Scientists unravel how and why Amazon trees die

The capacity of the Amazon forest to store carbon in a changing climate will ultimately be determined by how fast trees die — and what kills them. Now, a huge new study has unravelled what factors control tree mortality rates in Amazon forests and helps to explain why tree mortality is increasing across the Amazon basin.

This large analysis found that the mean growth rate of the tree species is the main risk factor behind Amazon tree death, with faster-growing trees dying off at a younger age. These findings have important consequences for our understanding of the future of these forests. Climate change tends to select fast-growing species. If the forests selected by climate change are more likely die younger, they will also store less carbon.

The study, co-led by the Universities of Birmingham and Leeds in collaboration with more than 100 scientists, is the first large scale analysis of the causes of tree death in the Amazon and uses long-term records gathered by the international RAINFOR network.

The results published in Nature Communications, show that species-level growth rates are a key risk factor for tree mortality.

“Understanding the main drivers of tree death allows us to better predict and plan for future trends — but this is a huge undertaking as there are more than 15,000 different tree species in the Amazon,” said lead author Dr Adriane Esquivel-Muelbert, of the Birmingham Institute of Forest Research.

Dr David Galbraith, from the University of Leeds added “We found a strong tendency for faster-growing species to die more, meaning they have shorter life spans. While climate change has provided favourable conditions for these species, because they also die more quickly the carbon sequestration service provided by Amazon trees is declining.”

Tree mortality is a rare event so to truly understand it requires huge amounts of data. The RAINFOR network has assembled more than 30 years of contributions from more than 100 scientists. It includes records from 189 one-hectare plots, each visited and monitored on average every 3 years. Each visit, researchers measure all trees above 10cm in diameter as well as the condition of every tree.

In total more than 124,000 living trees were followed, and 18,000 tree deaths recorded and analysed. When trees die, the researcher follows a fixed protocol to unravel the actual cause of death. “This involves detailed, forensic work and amounts to a massive ‘CSI Amazon’ effort conducted by skilled investigators from a dozen nations,” noted Prof. Oliver Phillips, from the University of Leeds.

Dr Beatriz Marimon, from UNEMAT, who coordinates multiple plots in central Brazil added: “Now that we can see more clearly what is going on across the whole forest, there are clear opportunities for action. We find that drought is also driving tree death, but so far only in the South of the Amazon. What is happening here should serve as an early warning system as we need to prevent the same fate overtaking trees elsewhere.”

Story Source:

Materials provided by University of Birmingham. Note: Content may be edited for style and length.

#Nature

#Nature

[techruman]

Running a Random Forest

Hey guy’s welcome back in previous blog we have seen that, how to run classification trees in python you can check it here. In this blog you are going to learn how to run Random Forest using python.

So now let’s see how to generate a random forest with Python. Again, I’m going to use the Wave One, Add Health Survey that I have data managed for the purpose of growing decision trees. You’ll recall that there are several variables. Again, we’ll define the response or target variable, regular smoking, based on answers to the question, have you ever smoked cigarettes regularly? That is, at least one cigarette every day for 30 days.

The candidate explanatory variables include gender, race, alcohol, marijuana, cocaine, or inhalant use. Availability of cigarettes in the home, whether or not either parent was on public assistance, any experience with being expelled from school, age, alcohol problems, deviance, violence, depression, self esteem, parental presence, activities with parents family and school connectedness and grade point average.

Much of the code that we’ll write for our random forest will be quite similar to the code we had written for individual decision trees.

First there are a number of libraries that we need to call in, including features from the sklearn library.

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

Next I’m going to use the change working directory function from the OS library to indicate where my data set is located.

os.chdir(“C:\TREES”)

Next I’ll load my data set called tree_addhealth.csv. because decision tree analyses cannot handle any NAs in our data set, my next step is to create a clean data frame that drops all NAs. Setting the new data frame called data_clean I can now take a look at various characteristics of my data, by using the D types and describe functions to examine data types and summary statistics.

#Load the dataset

AH_data = pd.read_csv(“tree_addhealth.csv”) data_clean = AH_data.dropna()

data_clean.dtypes data_clean.describe()

Next I set my explanatory and response, or target variables, and then include the train test split function for predictors and target. And set the size ratio to 60% for the training sample, and 40% for the test sample by indicating test_size=.4.

#Split into training and testing sets

predictors = data_clean[[‘BIO_SEX’,'HISPANIC’,'WHITE’,'BLACK’,'NAMERICAN’,'ASIAN’,'age’, 'ALCEVR1’,'ALCPROBS1’,'marever1’,'cocever1’,'inhever1’,'cigavail’,'DEP1’,'ESTEEM1’,'VIOL1’, 'PASSIST’,'DEVIANT1’,'SCHCONN1’,'GPA1’,'EXPEL1’,'FAMCONCT’,'PARACTV’,'PARPRES’]]

targets = data_clean.TREG1

Here I request the shape of these predictor and target and training test samples.

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

From sklearn.ensamble I import the RandomForestClassifier

#Build model on training data from sklearn.ensemble import RandomForestClassifier

Now that training and test data sets have already been created, we’ll initialize the random forest classifier from SK Learn and indicate n_estimators=25. n_estimators are the number of trees you would build with the random forest algorithm.

classifier=RandomForestClassifier(n_estimators=25)

Next I actually fit the model with the classifier.fit function which we passed the training predictors and training targets too.

classifier=classifier.fit(pred_train,tar_train)

Then, we go unto the prediction on the testator set. And we could also similar to decision tree code as for the confusion matrix and accuracy scores.

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

For the confusion matrix, we see the true negatives and true positives on the diagonal. And the 207 and the 82 represent the false negatives and false positives, respectively. Notice that the overall accuracy for the forest is 0.84. So 84% of the individuals were classified correctly, as regular smokers, or not regular smokers.

Given that we don’t interpret individual trees in a random forest, the most helpful information to be gotten from a forest is arguably the measured importance for each explanatory variable. Also called the features. Based on how many votes or splits each has produced in the 25 tree ensemble. To generate importance scores, we initialize the extra tree classifier, and then fit a model. Finally, we ask Python to print the feature importance scores calculated from the forest of trees that we’ve grown.

# fit an Extra Trees model to the data model = ExtraTreesClassifier() model.fit(pred_train,tar_train) # display the relative importance of each attribute print(model.feature_importances_)

The variables are listed in the order they’ve been named earlier in the code. Starting with gender, called BIO_SEX, and ending with parental presence. As we can see the variables with the highest important score at 0.13 is marijuana use. And the variable with the lowest important score is Asian ethnicity at .006.

As you will recall, the correct classification rate for the random forest was 84%. So were 25 trees actually needed to get this correct rate of classification? To determine what growing larger number of trees has brought us in terms of correct classification. We’re going to use code that builds for us different numbers of trees, from one to 25, and provides the correct classification rate for each. This code will build for us random forest classifier from one to 25, and then finding the accuracy score for each of those trees from one to 25, and storing it in an array. This will give me 25 different accuracy values. And we’ll plot them as the number of trees increase.

“”“ Running a different number of trees and see the effect of that on the accuracy of the prediction ”“”

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)):  classifier=RandomForestClassifier(n_estimators=idx + 1)  classifier=classifier.fit(pred_train,tar_train)  predictions=classifier.predict(pred_test)  accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

As you can see, with only one tree the accuracy is about 83%, and it climbs to only about 84% with successive trees that are grown giving us some confidence that it may be perfectly appropriate to interpret a single decision tree for this data. Given that it’s accuracy is quite near that of successive trees in the forest.

To summarize, like decision trees, random forests are a type of data mining algorithm that can select from among a large number of variables. Those that are most important in determining the target or response variable to be explained.

Also light decision trees. The target variable in a random forest can be categorical or quantitative. And the group of explanatory variables or features can be categorical and quantitative in any combination. Unlike decision trees however, the results of random forests often generalize well to new data.

Since the strongest signals are able to emerge through the growing of many trees. Further, small changes in the data do not impact the results of a random forest. In my opinion, the main weakness of random forests is simply that the results are less satisfying, since no trees are actually interpreted. Instead, the forest of trees is used to rank the importance of variables in predicting the target.

Thus we get a sense of the most important predictive variables but not necessarily their relationships to one another.

Complete Code

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics # Feature Importance from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

os.chdir(“C:\TREES”)

#Load the dataset

AH_data = pd.read_csv(“tree_addhealth.csv”) data_clean = AH_data.dropna()

data_clean.dtypes data_clean.describe()

#Split into training and testing sets

predictors = data_clean[['BIO_SEX’,'HISPANIC’,'WHITE’,'BLACK’,'NAMERICAN’,'ASIAN’,'age’, 'ALCEVR1’,'ALCPROBS1’,'marever1’,'cocever1’,'inhever1’,'cigavail’,'DEP1’,'ESTEEM1’,'VIOL1’, 'PASSIST’,'DEVIANT1’,'SCHCONN1’,'GPA1’,'EXPEL1’,'FAMCONCT’,'PARACTV’,'PARPRES’]]

targets = data_clean.TREG1

pred_train, pred_test, tar_train, tar_test  = train_test_split(predictors, targets, test_size=.4)

pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#Build model on training data from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train)

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

# fit an Extra Trees model to the data model = ExtraTreesClassifier() model.fit(pred_train,tar_train) # display the relative importance of each attribute print(model.feature_importances_)

“”“ Running a different number of trees and see the effect of that on the accuracy of the prediction ”“”

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)):  classifier=RandomForestClassifier(n_estimators=idx + 1)  classifier=classifier.fit(pred_train,tar_train)  predictions=classifier.predict(pred_test)  accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy)

If you are still here, I appreciate that, and see you guy’s next time. ✌️

[bisonhillock]

Internship adventures during summer ‘16

From June till October I have been doing my internship at the ‘Bison Hillock’ project in the south-western part of Romania. This internship was a part of my Master study Biology at the Wageningen University in The Netherlands, where one my teachers got me in contact with Rewilding Europe. They suggested to me to check out their rewilding project in the Southern Carpathians and connected me to the WWF Romania team that is working there. Soon it became clear that this project could really use an intern student, as there were special events about to happen, so I went for it!

In the beginning of June 2016 I arrived in a small and rural Romanian village called Fenes. Here, most people work on their lands in the surrounding hills, and with only one primary school, a few shops and a small monastery, many people spend their free time by sitting outside in front of their houses, watching as the day goes by. At first sight, it seems like nothing special is happing there, but there is! Fenes is home to the research station of the WWF Romania’s team members of the Bison Hillock project. The project is aimed to bring back the European bison (Bison bonasus) to the Romanian wilderness. As part of a bigger plan, these bison will be free to roam the largest European mountain range and hopefully connect with other reintroduced bison herds from several Eastern European countries to restore a viable and healthy bison population in Europe.

The rewilding takes place in stages, where bison are released into an enclosure with a so-called ‘acclimatization zone’ and ‘rewilding zone’, before being released into the wild. Just a few days after my arrival, the first 20 bison that were brought to this enclosure over the last years, were set free. This was great news and a big step forward in the project! Shortly afterwards a new group of 10 bison arrived from Belgium and Germany to rewild during the following months. Television crews from CNN, Belgium and Romania joined this event, as did the involved partners and locals. It was great to see that many people are interested and involved in this project!

With the first group of bison enjoying their freedom I was able to start my research. Because little is known about the bison behaviour in the wild, it was important to find out which places and habitats these bison preferred, described in terms of their habitat use. Would they move to the higher elevated mountainous forests and grasslands? Or maybe stick around the local farms and pastures? Collected data would not only provide information that can be used for adaptive management for example to prevent future human-bison conflicts, but also for the search of alternative reintroduction sites along the Carpathians to support the establishment of a healthy bison population. So, I used the available methods to collect data on where the bison were hanging around. I analysed GPS data from a collared bison, videos from camera traps in the study area, and coordinates from indirect bison observations by performing transects and other hikes. These indirect observations included the tracks and faeces of the bison, but also the damage to the bark of trees, as the bison like to nibble on the bark of young beech (Fagus sylvatica) and fir (Abies alba) saplings.

Every time I went up into the mountains to do my fieldwork research I felt privileged to be working in such a beautiful and wild place. Each week I hiked several tens of kilometres through the area to cover as much ground as possible to track the wild bison and conduct my research. Of course the bison are not the only wildlife that can be found there, as the camera traps have captured species of deer, wild boar, wolf, brown bear, and even the illusive Eurasian lynx! One time I even had a close encounter with a large mammal, hiding just 20 meters away in a dense beech-seedling patch. Although I couldn’t see what was running away from me, I found very fresh bear scats just a hundred meters away! But besides the fact that this was quite exciting it was actually very useful, as I was able to collect samples for another study on genetic diversity on the brown bear in the Southern Carpathians!

After collecting all my data I analysed it with statistical and GIS-software and I found several ‘hotspots’ where bison-activity was very high! Interestingly, some bison that were found often in these hotspots preferred pasture habitats and spend more than 70% of their time in this habitat type. Remarkably, these findings differ from studies on habitat use of the free bison in Poland, where they spent most of their time in forest habitats. But not all the bison were found in pastures, as many tracks were found in other (open) forest areas, indicating that another part of the group is expanding the home range and exploring the area. This is promising news and hopefully the bison will keep exploring to find their way across the mountains in the next years!

Besides doing research on the bison, the Bison Hillock project is also focussing on other aspects like involvement and development of local enterprises, raising awareness for nature conservation, and the establishment of nature-friendly tourism. During my time in Romania I’ve helped to set up a weekly movie-night for the local youth, where we mixed educative nature related documentaries with Disney movies to keep the kids involved and familiar with the project. Also the visitor centre in the neighbouring Armeniș village has been opened with state of the art installations where science is displayed to anyone interested. It even hosts Europe biggest hologram-projection, where you can see the bison moving around!

The Bison Hillock project has proven to me to be special and unique, a place you must visit if you are interested in the European wilderness! The project enables people to stand at the frontline of nature conservation and learn about its beauty and challenges. Personally, I have learnt a lot and I was happy to be able to teach other people who are involved in the project too! I am sure that more special events will take place in the upcoming years, so I would recommend everybody to support and visit this magnificent place in the green heart of Europe! I thank WWF Romania and Rewilding Europe once again for giving me the opportunity to join their work to make Europe a wilder place!

#Rewilding #bisonhillock #magurazimbrilor #wwf #internship

[scienceblogtumbler]

‘Four times more toxic’: How wildfire smoke ages over time

Enormous plumes of smoke thrown into the atmosphere by uncontrolled wildfires may be affecting the health of people living hundreds of miles away.

Every year, thousands of fires engulf forests, grasslands and moors across Europe. In 2018, more than 204,861 hectares of land were left burnt in Europe and other countries around the Mediterranean, while the previous year wildfires destroyed over 1.2 million hectares. Blazes in the Arctic in June set a new record in carbon emissions in 18 years of monitoring.

As the trees, shrubs, grass and peat are engulfed by these fires, huge quantities of smoke, soot and other pollutants are released into the air. With large fires, the smoke can rise many kilometres into the stratosphere and spread across entire regions, causing air pollution in areas far away from where the flames actually were.

‘In the eastern Mediterranean we get smoke that blows down from forest fires in Russia and when it happens there is just hazy smoke everywhere,’ said Professor Athanasios Nenes, an atmospheric chemist at the Institute of Chemical Engineering Sciences in Patras, Greece. ‘It can be quite dramatic. They are affecting air quality over entire regions or parts of continents.’

Prof. Nenes is principal investigator of the PyroTRACH project, which is attempting to find out how emissions from wildfires – along with other types of biomass burning, such as domestic wood fires – change in the atmosphere and the impact this has on human health and climate.

Globally, wildfire smoke is estimated to cause over 339,000 premature deaths a year – far more than those who lose their lives directly in these blazes.

The team is taking regular air samples at three locations in Greece – Crete, Athens and Patras. These are being combined with samples provided by collaborators around the world including in the US, the Arctic, India, Europe, Vietnam and in the air above the south Atlantic Ocean.

‘When you look at these samples, you can always find a lot of particles in the air, but you can’t say for sure whether it has come from biomass burning,’ said Prof. Nenes. ‘The idea behind PyroTRACH is to see if we can identify specific signatures of fires and, in addition, track what happens to the smoke as it ages in the atmosphere.’

Air pollution picked up by samplers, such as the one in Patras, Greece, could have come from fires thousands of kilometres away. Image credit – Spiro Jorga

Age

To do this, the researchers are using a special environmental chamber in the laboratory that replicates the conditions found in the atmosphere. They then generate fresh smoke samples by burning different types of plant material, which are then allowed to “age” in the chamber.

Over time they are able to see how the chemistry of the particles in the smoke changes when exposed to the atmosphere and daily patterns of sunlight and darkness. Portable chambers also allow them to age smoke directly produced from real fires in the outside environment.

‘We are trying to understand the lifetime of smoke in the atmosphere and how it chemically evolves,’ said Prof. Nenes. ‘We want to characterise the impacts it will have on human health and the climate. Does it become more toxic (with age), or have a greater (warming) effect on the climate (than currently thought), or supply more nutrients to ecosystems when it falls back to the ground?’

One of the key findings the team has made since the five-year project began in 2017 is that particles released from burning vegetation in forest fires become more toxic over time.

Smoke from forest fires can linger in the atmosphere for a couple of weeks as it spreads. While in the air the smoke particles chemically react with trace radicals – molecules with unpaired electrons – to undergo a process known as oxidation. This converts the compounds in the smoke particles into highly reactive compounds. When they are breathed in, these reactive compounds – known as free radicals – can damage cells and tissues in the body.

‘We know that breathing in smoke when you are close to a fire is not good, but we have seen that over time it gets worse – up to four times more toxic a day down the road,’ said Prof. Nenes, referring to some of their experiment results. These results showed smoke samples taken from the air more than five hours after they were released from a fire were twice as toxic than when they were first released and as they aged further in the laboratory the toxicity increased to four times the original levels.

‘This means that even if you are far away from a fire, if the smoke is being blown towards you, it can have a significant impact on health,’ he said. ‘People might not even be aware they are breathing in the fumes from a faraway forest fire, but it will be affecting their health.’

‘People might not even be aware they are breathing in the fumes from a faraway forest fire, but it will be affecting their health.’

Professor Athanasios Nenes, Institute of Chemical Engineering Sciences, Greece

Health

While the exact health effects of breathing in this smoke are still to be fully understood, Prof. Nenes and his team will feed their results into another project called REMEDIA. It is looking at how air pollution affects the lungs as part of the Human Exposome Network, which focuses on what environmental exposures do to human health.

But reactive compounds from wildfire smoke are thought to have a number of short and long-term health effects.

‘They can make people more prone to infections, can lead to breathing difficulties and leave some people more prone to heart attacks,’ said Prof. Nenes. ‘At the same time the smoke particles also contain carcinogens – polyaromatic hydrocarbons – which also oxidise and become more carcinogenic, increasing the risk of cancers.’

This increase in toxicity is a particular concern as smoke from large wildfires is known to travel across whole continents and even oceans. Smoke billowing from forest fires in Alberta, Canada, for example, was tracked as spreading down the east coast of the US, across the Atlantic and into Europe in 2019. Similarly, smoke from the recent devastating fires in Australia engulfed South America and pollution from wildfires in Siberia have spread to western Canada and the US.

‘Wildfire smoke can circulate the globe,’ said Dr Mike Flannigan, director of the Canadian Partnership for Wildland Fire Science at the University of Alberta. ‘Smoke from intense fire can be injected into the upper atmosphere where strong winds – typically west winds – can carry it rapidly around the world.’

This means that large wildfires can have dramatic impacts on the air quality and visibility in cities far away from the source of the smoke, which can then make urban air pollution worse, increasing the risk of health problems and deaths among those living there.

Samples of air in downtown Athens, Greece, along with others from around the world, are being analysed to see if signature particles from wildfires can be identified. Image credit – Irini Tsiodra

Smoke

In Europe there are on average 65,000 wildfires every year, but the region is also engulfed by seasonal clouds of smoke from blazes further afield too.

Through the colder winter months domestic wood burning contributes a significant fraction of the smoke in the atmosphere, particularly in urban areas, according to Prof. Nenes.

More work is needed to understand the many sources of pollution in the air. Unravelling these sources is the goal of the Aeromet project. It is developing new ways of better analysing the aerosols and particles that pollute the air, particularly in urban areas across Europe. Currently it is difficult to distinguish which come from natural sources – such as dust blown into the air and salt lifted off the oceans by the wind – and those that come from fires, vehicles, industry and other human activities.

Improving the accuracy of how these are measured and identified could not only help authorities monitor air pollution better, but also ‘potentially help to identify critical single sources of particles and to propose appropriate counter-measures to improve air quality’ based upon the findings, says Dr Burkhard Beckhoff, coordinator of the Aeromet project and a researcher at Germany’s Physikalisch-Technische Bundesanstalt in Berlin.

Prof. Nenes hopes that characterising the pollution from wildfires and domestic woodburning could help to improve climate change models as some of the soot released by fires – known as brown carbon – plays a considerable role in absorbing heat from the sun, and makes global warming worse.

‘The smaller brown carbon molecules tend to bleach quite quickly but larger ones are more resilient, creating a low but persistent heating effect,’ he said.

Knowing how much of this brown carbon is produced in wildfires and domestic woodburning would allow climate scientists to make better climate predictions.

With climate models already predicting that wildfires are likely to become more common and intense as global temperatures increase, and domestic wood burning on the rise, the smoke they produce could pose an even greater risk to human health and the environment, says Prof. Nenes.

‘I grew up being able to see the effect fires have on our air here in Greece,’ he said. ‘It is alarming to think about what we are doing to ourselves and the environment. But hopefully as we understand more about this, we can contribute to policies that should be developed to help mitigate the impact of these fires.’

The research in this article was funded by the EU. If you liked this article, please consider sharing it on social media.

Published by Horizon 

source https://horizon.scienceblog.com/1360/four-times-more-toxic-how-wildfire-smoke-ages-over-time/

[barbosaasouza]

Hearthstone: The Witchwood - The GDC 2018 Interview

The card reveals for Hearthstone's next expansion, The Witchwood, are officially underway. Members of Blizzard's Team 5 went to Twitch earlier on Monday to kick off the card reveals in earnest, which Shacknews will be following throughout the coming weeks.

But before continuing our card analyses, Shacknews recently went to this year's Game Developers Conference. That's where we found Hearthstone Lead Mission Designer Dave Kosak (fresh off his time lost in the woods) and Game Designer Dean Ayala, who were happy to discuss the upcoming Witchwood expansion. We made sure to ask about some of its new mechanics, the odd/even deck synergy, and tried to gather some more information about the mysterious Hagatha.

Shacknews: How did you decide on The Witchwood for Hearthstone's next expansion?

Dave Kosak, Lead Mission Designer: It's a very collaborative process. We actually have full team brainstorming. "What are we going to do next?" There was like a zillion ideas. The Warcraft franchise is wide and deep, there's so much to do.

But the important thing to do is mix it up and create a lot of different feelings and vibes. We had a very dark expansion with Knights of the Frozen Throne, where everybody turns into Death Knights. And then we had a romp, what I like to call a dungeon romp with Kobolds & Catacombs. The bad guys were kobolds and there was something very, very different than the Lich King. So we wanted to mix it up a little. What's going to feel really different? So we wanted to do something scary. This is kind of our scary, spooky expansion about a twisted forest and ghosts haunting things.

That's the feeling we wanted to create. By setting it in Gilneas, we get to create a monster versus monster vibe by having the Gilneans there. The Gilneans have a curse. They turn into Worgen, which is basically the WoW equivalent of werewolves, which is great! Because it's not like this helpless town being attacked by monsters. They are, in fact, themselves monsters and that creates a really fun vibe for us.

Shacknews: One of the bolder new ideas for the new expansion is pushing forward the idea of odd and even decks. How did the team come up with this idea and are there some examples of decks that players can utilize?

Dean Ayala, Game Designer: We had a bunch of inspirations for different types of cards, but one of the biggest ones were Kazakus and Reno. The cards that have you construct a deck with no duplicates. It presents a really interesting challenge, where even in the Collection Manager, you're really thinking about not only what kind of decks can utilize these cards the best, but also what kinds of decks are restricted the least by this restriction.

So being able to do that in the Collection Manager is a really interesting challenge, so we're just trying to figure out how to build these interesting deck-building challenges. But also with the slight twist that these upgrades are so powerful, having a 1-Cost Hero Power or upgraded Hero Power is really such a powerful thing to have. The payoff has to be really big for such a huge downside, so having them trigger at the start of the game can sort of alleviate the pressure of feeling that if you don't draw these cards, you're at a disadvantage. Having these triggers at the start of the game is really important for that, so we can balance around the idea that you have these upgrades the entire game.

In terms of decks, we talked about Face Hunter and Quest Warrior. I think both versions of Paladin are quite powerful. Surprisingly, I think odd-Cost Paladin is one of the more powerful ones, which is strange, because cards like Call to Arms, Sunkeeper Tarim, and Tirion are all cards that you can't include in your deck. But being able to make two Silver Hand Recruits every time you use your Hero Power and you still get to keep your own cards like Level Up, Lost in the Jungle, Righteous Protector, and a variety of one-Cost cards. I think that deck is still very powerful, especially with Vinecleaver.

Shacknews: Given that the team is really pushing forward this idea of experimenting with odd and even decks, is it safe to assume that Baku the Mooneater and/or Genn Greymane will be easy to acquire or even possibly offered for free as part of the new expansion?

Kosak: You get a free Legendary as part of logging into the expansion, but I think it's a class Legendary, so it wouldn't be one of these guys.

Ayala: The cost of Hearthstone is always on the front of our minds in terms of delivering a fair experience to the players. Doing things like getting a free legendary when you log in, getting three free packs when you log in, a lot of our quests are going to be 50 Gold and going to be easier to complete. Just the idea that we're continuing to monitor the amount of content that we deliver you per expansion and doing stuff like the pre-order, where it's 50 packs with a bonus 20 packs, is a big help. So we're just continuing to see what are the things that we can do to give away a bunch of fun stuff. When you have a bunch of fun stuff, you can decide for yourself what you want to play.

Shacknews: At the end of the expansion reveal, you all ran away screaming from 'Hagatha.' Is there any hint as to what 'Hagatha' could be?

Kosak: Let's see... Hagatha is going to be our centerpiece villain of the expansion. She is who has corrupted these woods and warped and twisted the trees, beasts, and elements to her will. She is the witch of the Witchwood. It's hard to tell in the video, but most players squinted their eyes and saw that it was a Shaman Hero Card. We'll reveal her in time, but she is definitely one of our marquis villains and the only Hero Card of the set.

(Editor's Note: Hagatha the Witch was officially revealed during the Monday morning card reveal stream, roughly a week after this interview was originally conducted. Let's look at her in action!)

Shacknews: Is there a reason why she was picked for Shaman over the other classes?

Ayala: Flavor-wise, if you're a witch, Shamans have that elemental and, not exactly "wizard" vibe, but magic-using, witch doctor...

Kosak: Elementals, evolves, and devolves...

Ayala: It just fits with her character.

Shacknews: And for Monster Hunt, why opt to use new Heroes as opposed to the established nine classes?

Kosak: We love the Dungeon Run and it turned out the Dungeon Run was very successful. We were looking for ways to change it up, but also keep the format, because the format worked really well. We actually experimented with a lot of ways of changing it up and gradually scaled back to what we thought was the most fun, but didn't add tons of complexity. So it'll function almost the same way, you'll be fighting eight bosses with escalating difficulty, you'll still be finding treasures, building your deck as you go, but now you have the four special heroes.

The four special heroes play with the same class cards, they are an existing class, but with a unique Hero Power and that gave you some really different gameplay. They also have some unique treasures for that hero. So as you're finding treasures, you might find something just for them to use specifically. That let us do some really cool stuff to sort of change up the gameplay. Even though mechanically it's very much like the Dungeon Run, thematically and in the way that these heroes play, it feels really new. We thought it felt great as it was. We call the Monster Hunt, because instead of delving into a dungeon, you are wading out into the forest and hunting these monsters that everyone else is afraid of.

It also gave us some new storytelling opportunities, because we can really have these four heroes from Gilneas or the parts around Gilneas and their own interactions and reactions with the monsters and their own story that plays out. That turned out to be super fun for us. So I think players will have a lot of fun exploring the Witchwood with these characters.

Shacknews: Should players expect to see Monster Hunt as part of their daily quests, just like Dungeon Run?

Kosak: The current plan is that there will be a similar quest to delve back into the woods.

Shacknews: Feature-wise, I wanted to ask about in-game tournaments and what the current plan for that is.

Kosak: This is a feature that we've wanted for a long time and our players have wanted for a long time. If you ask players if they want tournaments in the game, they're like, "Yes!" And if you ask specifically what they would like to see, everyone has a different answer.

So this is a feature that we're going to be working on for a long time. The first beta will come out this year. It'll allow you to set up a tournament, invite your friends, and play through the tournament and have a winner. And it'll just sort of be the start of a feature that we're going to continue to iterate it, because it's a much-requested feature and there's lots of different elements that people want in this feature. That'll be in beta later this year and we'll have more information as we get a little closer.

Shacknews: Is this something you hope to see catch on at Fireside Gatherings? And will Twitch streamers be able to use it to throw their own tournaments with their viewers?

Kosak: It'll definitely work at Fireside Gatherings. I don't see any reason why a Twitch streamer couldn't use it. It may not be the perfect tool...

Ayala: We've used it at Fireside Gatherings. It seems like that would be the more reasonable place to use it.

Shacknews: Lastly, I want to go off on a bit of a tangent. I started thinking about this when I put together the Hagatha question. I wanted to ask about Lord Jaraxxus, because I wondered if the team has ever thought about making him into a Hero Card, given how well the Hero Card mechanic has gone over?

Kosak: It would change his mechanic some, because as a minion, he does certain things, minion-y things. He can be Recruited... which is generally an "Oh no!" kind of moment. He plays a certain way now and if we made him a Hero Card, we'd change a lot of what makes Jaraxxus very Jaraxx-y.

Ayala: Jaraxxus is one of the most iconic cards in all of Hearthstone, too.

Kosak: We'd be really reluctant to change it just because we have a new thing that he could be.

Ayala: I could see Jaraxxus coming back in a different form someday, but the original form of Jaraxxus... I think he's safe.

Look for more on Hearthstone's Witchwood expansion in the comings weeks. Now that the card reveals have begun, Shacknews will be getting to work on the next round of card analyses. And be sure to come back on April 3, because Shacknews will have an exclusive Witchwood reveal of our own.

Hearthstone: The Witchwood - The GDC 2018 Interview published first on https://superworldrom.tumblr.com/