Kenneth De Baets

@djbirddanerd.bsky.social

Paleobiologist @ibe-warszawa.bsky.social into cephalopods, parasites, funny tees and movies; Paleontology/Evolution Section Editor @PeerJLife ; previously @palaeofau. Avatar after Jacek Yerka also on @djbirddanerd@ecoevo.social

Our new paper introduces a fast & Bayesian state-dependent OU model in RevBayes that implements joint inference, to test adaptive macroevolutionary hypotheses. Congrats @prilau.bsky.social! Be sure to give her a follow & give her original tweet some love! Many more exciting PCMs in her future!

Priscilla Lau@prilau.bsky.social · 6d ago

Our paper (also, my first first-author paper) is now available in MEE early view! In this study, we introduced a state-dependent Ornstein-Uhlenbeck model we implemented in RevBayes to test adaptive hypotheses in macroevolution. (1/3) besjournals.onlinelibrary.wiley.com/doi/10.1111/...

Know an undergrad attending GSA 2026? Share this opportunity with them! 🤝 Students selected are expected to engage in mentoring opportunities throughout the meeting, 5 hours of volunteering at the PS booth, and interacting with our social media team.

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My joint work w/ Matteo Bedetti is now officially out in Biology & Philosophy! [Re-sharing the 🧵, with full-text link 👇] We examine how evidential standards are negotiated in funerary #archaeology debates, using the Homo #naledi burial controversy as a case study in epistemological practice.🧪🏺💀

Buried, or maybe not: adherence to evidential standards in paleoanthropology - Biology & Philosophy

This paper examines how evidential standards are constructed, interpreted, and enforced in paleoanthropology and funerary archaeology, focusing on claims of deliberate burial practices in Homo naledi....

link.springer.com

1. A bit of evolutionary biology. I'm really intrigued by a new perspective piece from Steve Frank that explores connections between how natural selection creates systems that generalize and recent work in machine learning about the surprising capabilities of massively overparameterized systems.

Generalization as the great leap in evolvability: insights from machine learning

Abstract. Natural selection encodes learned information in the genome. Learned solutions may be tuned specifically to past challenges, failing in altered e

academic.oup.com