Optimisation of energetic and reproductive gains explains behavioural responses to environmental variation across seasons and years
Bibliographic record
Abstract
Abstract Animals switch between inactive and active states, simultaneously impacting their energy intake, energy expenditure and predation risk, and collectively defining how they engage with environmental variation and trophic interactions. We assess daily activity responses to long‐term variation in temperature, resources and mating opportunities to examine whether individuals choose to be active or inactive according to an optimisation of the relative energetic and reproductive gains each state offers. We show that this simplified behavioural decision approach predicts most activity variation ( R 2 = 0.83) expressed by free‐ranging red squirrels over 4 years, as quantified through accelerometer recordings (489 deployments; 5066 squirrel‐days). Recognising activity as a determinant of energetic status, the predictability of activity variation aggregated at a daily scale, and the clear signal that behaviour is environmentally forced through optimisation of gain, provides an integrated approach to examine behavioural variation as an intermediary between environmental variation and energetic, life‐history and ecological outcomes.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".