Episode 121: Stickle-back to the future: experimental evolution in nature (with Alison Derry and Andrew Hendry)
Bibliographic record
Abstract
Can we study evolution in the wild? Are some species “super-evolvers”? On the episode, we talk with Alison Derry, a professor of biology at the University of Quebec in Montreal, and Andrew Hendry, a professor in the Department of Biology at McGill University, Canada. This episode is the second we’ve done on the team’s work, and Andrew was also a guest on our first episode in the series. This conversation was recorded live in front of an audience at Kenai Peninsula College, in Soldotna, Alaska. The college is just a few miles from the lakes where Alison, Andrew, and many of their colleagues and students carry out experiments on threespine sticklebacks. We ask Alison and Andrew about their research on the rapid evolution of these fish, which were recently reintroduced to the lakes, and how the introduction of two distinct stickleback ecotypes are affecting the evolution of zooplankton in the lakes. We also discuss the central position of sticklebacks in the food web and how the sticklebacks are impacting the ecosystems now as well as how they likely impacted the lakes in the evolutionary past. Cover photo: Keating Shahmehri. Audio from Hunter Morrison at KDLL.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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 source (direct Gemma or distilled Codex), 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".