Wild <i>Peromyscus</i> adjust maternal nest-building behaviour in response to ambient temperature
Bibliographic record
Abstract
The heat dissipation limit (HDL) hypothesis suggests that energy output during lactation in mammals might be constrained by their ability to dissipate heat. This hypothesis predicts that wild mammals ought to adjust nest insulation in response to heat load, but these predictions have rarely been tested in wild mammals. Here we developed a simple score of nest-building for wild deer mice (Peromyscus maniculatus (Wagner, 1845)) on an ordinal scale from 0 to 4, based on three qualitative and easy to observe aspects of nest-building behaviour: bedding quality, nest shape, and mouse visibility. We used this measure to track 472 nest-building observations across 14 wild P. maniculatus that were brought into captivity and housed under pseudoambient temperatures across one reproductive event. Our observations of nest-building behaviour of the genus Peromyscus Gloger, 1841 provide varying support for the HDL hypothesis; there is a negative effect of ambient temperature on nest-building behaviour and lactating females became more sensitive to temperature as days post partum increased. However, females generally build more elaborate nests in lactation than other reproductive states and there are no effects of litter size, total pup mass, or days post partum on nest scores during lactation. Our observations have broad implications for quantifying behaviours in nest-building species and metabolic relationships in wild mammals.
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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 | 0.000 | 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) | 0.001 | 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 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".