Episode 106: Long-term experimental evolution in the wild (with Katie Peichel and Andrew Hendry)
Bibliographic record
Abstract
Can we predict evolutionary outcomes if we know starting conditions? Do the products of evolution in nature differ from those studied in well-controlled lab experiments? On this episode, we talk to Katie Peichel, head of the Division of Evolutionary Ecology at the University of Bern, Switzerland, and Andrew Hendry, professor in the Department of Biology at McGill University, Canada. Katie and Andrew are part of a massive research team working on the evolution of threespine sticklebacks as they are reintroduced into lakes in Alaska. Sticklebacks have been a favorite species for evolutionary biologists since almost the origins of modern evolutionary theory. Traits like spine size and lateral plate armor evolve rapidly when populations colonize new habitats, leading populations to barely resemble one another. Unlike traditional evolutionary experiments, which try to infer what occurred in the past, the Alaska project is tracking in unparalleled detail changes in the phenotypes and genotypes of fish that went into each lake population. We talk to Katie and Andrew about the origins of this incredible project, the pros and cons of different approaches to studying evolution, and the need for long-term experimental studies of evolution in the wild. This is the first of a series of episodes we will be doing on the Alaskan research project, so stay tuned! Cover photo: Keating Shahmehri
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".