Jeremy Lefort-Besnard

@jlefortbesnard.bsky.social

Edge AI https://jlefortbesnard.fr

This looks useful. A typical meta-analysis in ecology mixes experiments with observational studies, mixes coefficients with different controls and therefore different causal meanings. I would not normally say "hey the folks in medicine are doing it right, imitate them" but in this case maybe

Alfredo Sánchez-Tójar@asanchez-tojar.bsky.social · 4w ago

Are our ecological conclusions built on shaky foundations? Our latest paper in @methodsinecoevol.bsky.social highlights that Risk of Bias (RoB) assessment, crucial for ensuring the internal validity of research, is almost never used in #systematicreviews in eco & evo 📉 🔗 doi.org/10.1111/2041...

I think this kind of thing deserves a response that reveals the assumptions that make "Rabois World" correct, including no mediators or colliders. I don't have time for that though. So here's my old series on what's wrong with thinking like a regression: elevanth.org/blog/2021/06...

Regression, Fire, and Dangerous Things (1/3)

It isn't my job to disappoint people, but I'm good at it.

elevanth.org

Saloni@scientificdiscovery.dev · last mo.

Oh no! Oh no!!

In most cases, the more covariates we have in the data, the higher the chances we catch all confounders. (Overfitting can be a risk in prediction problems, but in public health there are usually too few rather than too many covariates in the model.) This is why, in a world with a growing wealth of data, observational evidence treated with causal methods ends up winning.

Cette animation est saisissante. Elle montre, en seulement quelques secondes, l'accélération spectaculaire de la fréquence des vagues de chaleur au cours des 126 dernières années en France. 1/3

I think of it as this: LLMs lower the barrier of entry, and they make coders (beginners and experts) more productive. It's still worth investing in becoming an expert, because then you will get even more out of LLMs and will be able to deliver even better results.

Bild

Interesting mystery. It is known that animals can learn to control neurons pretty much anywhere in the brain. But they can not learn to ignore hunger which probably means they can't turn of hunger sensing neurons. How is that avoided in the brain? They even have DA inputs.

I couldn't be more on board with this. Focusing on corruption/fraud/misconduct at the level of individual scientist appears to be misguided and counterproductive when systemic forces and selection biases are at play. This appears to be true even beyond the context of industry manipulation.

Joe Bak-Coleman@jbakcoleman.bsky.social · 10mo ago

Science has faced these challenges before. However, many think manipulation happens through corruption of scientists. Instead, we highlighted a broader range of mechanisms: -Burying Internal research -Selectively publishing -Design bias -Selective funding and access.