Using an integrative approach to investigate the evolution of behaviour
Bibliographic record
Abstract
Behaviour is a central focus of interest in biology because it has an impact on several aspects of an organism's life. Evolutionary biologists have realised the advantage of an integrative approach that jointly studies the molecular, cellular and physiological levels of an individual to link them with the organismal behavioural phenotype. First, this mechanistic information helps in understanding physiological and evolutionary constraints acting on the behavioural response to the environment and its evolution. Second, it furthers our understanding of the process of molecular convergent evolution. Finally, we learn about natural variation in molecular, cellular and physiological traits present in wild populations and their underlying genetic basis, which can be a substrate for selection to act on. I illustrate these points using our work on behaviour variation in fishes. The information on the mechanistic bases of behaviour variation in various species and behaviours will contribute to an ecological annotation of genes and to uncover new mechanisms implicated in how this astonishing behavioural diversity arose, is maintained and will evolve.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".