Профиль

13. Hence our dismal conclusion: rather than finding that LLMs free us to do a better job of what we were doing before they came along, they shift scientific incentives (and the playing field of academic competition) in ways that compel us to do more and more, faster and faster, less and less well.

Understood -- thanks for sharing, @erikvannimwegen.bsky.social !! That's a helpful perspective.

Awesome, thanks Mike!!! 😽😽

Thanks so much for sharing, @dutchscientist.bsky.social !! Much appreciated.

Super helpful to know -- thanks!! I appreciate you taking the time, Alex!! @acritschristoph.bsky.social

Many thanks to all for sharing, and feel free to email/DM me if you don't want to answer publicly. These chats really help me understand what you all need in terms of methods. So, thank you 🙏🏻🙏🏻 ❤️, Amy 7/

Without exaggeration, uncertainty quantification is my livelihood -- I'm a statistician, that's what we do. So, I know what these numbers mean to me... but what do they mean to you all, dear bacteria/microbiome friends, when you produce and read numbers that imply such confidence? 6/

(And yes, I am familiar with the debate about this paper and the Author Correction.) 5/

and almost all of these splits have >99% posterior probability (Fig 2 of the same paper) 4/

Here's what I'm talking about: Almost all of these splits have >90% bootstrap support (Fig 1 of www.nature.com/articles/s41...) 3/

Almost all of these numbers are >90%, >95%, >99%... That level of confidence may makes sense for more recent evolution, but the same confident numbers appear when we are talking divergences from billions and billions of years ago. 2/

Genuine question for folx who look at bacterial phylogenies: How do you interpret the bootstrap support and posterior probabilities that you get on your tree estimates? Do you take them seriously? Do you think: "Wow, all 99%, that must be a reliable tree!"? 1/

I'm hiring a #postdoc 🥳 and applications are open 🤩 Candidates with interests in EITHER statistical methodology or microbial ecology / microbiome are welcome to apply. The position / projects will be tailored to the candidate 🌟📈🎤 Thank you for sharing widely! apply.interfolio.com/188571

I'm hiring a #postdoc!! 🥳 Official ad is making its way through HR but If you have interests in statistical methodology OR microbial bioinformatics, please reach out via my UW email. I'll share the description with you when it's available. 🔥😻

This isn't a full-picture benchmark against all other methods BUT I am rather pleased and thought I would share 🥳 A great argument for radEmu's robust-centering approach to identifiability 🔥 and valid error rate control 😻 Those score tests ran overnight, but were definitely worth waiting for 🎤

radEmu took the cake today. I'm looking at a dataset where the truth is known (a complex spike-in, spiked-in at varying intensities). All taxa should be unchanging in abundance except for the spike-ins... True "positives": 6 / 1505 radEmu score tests identifies 7 CLR + linear model identifies 517

Are you doing prediction using microbial community data as input? Please report accuracy as *average percentage error* of the prediction relative to the measurement. I'm not interested in their correlation -- I want to know how many orders magnitude you're off by. Thx! #microbiome #deeplearning

We really appreciate your support, Kendra!!

Thx for checking! Co-author Teichman joined the team after v1. Here's what I'll be using: David S Clausen, Sarah V Teichman, & Amy D Willis. 2025+. "Estimating Ratios of Means of Multicategory Data Observed with Sample and Category Perturbations." Biometrika, In Press. arxiv.org/abs/2402.05231.

Documentation: statdivlab.github.io/radEmu/ Blog post: statdivlab.github.io/blog/article... 4/4