Clockwork precision: egg-laying-induced rise of body temperature is seasonally programmed in a wild bird
Bibliographic record
Abstract
There is long time interest about the phenology of plants and animals living in seasonal environments as research in that field would help to understand the coping mechanisms leading to a higher fitness. For instance, it has been shown several decades ago that birds prepare themselves 2–4 months before the actual start of the breeding season by slowly growing reproductive organs. In parallel, the resting metabolic rate increase during reproduction in various vertebrates including mammals, reptiles, and birds. Recently, it has been reported that body temperature of a marine bird species was reaching an annual peak during egg-laying, raising the question about the seasonal dynamic of this important physiological feature. Using data loggers implanted in the abdominal cavity of female Common Eiders (Somateria mollissima mollissima) for a full year, we show here that daily body temperature (Tb.daily) is slowly increasing first and then accelerating at the approach of the laying period. Because the rise of Tb.daily is tightly associated with egg-laying in this species, we also analysed the influence of ambient temperature (water and air) and photoperiod on this seasonal dynamic. Based on the various mechanisms at work and a parsimonious interpretation of the data, we conclude that photoperiod is the main cue driving the seasonal breeding program of eiders. Although the laying dates of the instrumented females were highly clustered over a period of 4 years, we speculated that the remaining variation observed was the result of eco-physiological challenges occurring over the years.
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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 | 0.001 |
| 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".