Data from: Long-term, not short-term, temperatures predict timing of egg-laying in European Starling (Sturnus vulgaris)
Bibliographic record
Abstract
Temperature is thought to be an important supplemental cue used by female birds to time breeding. It is widely assumed that late spring temperatures, within ~1 month of egg-laying, are most predictive of laying date, consistent with the idea that temperature is used to fine-tune timing of breeding to year-specific local conditions at the time of laying. Here we show that a relatively broad, long-term, temperature window (Jan 2 – Apr 4, 92 days; r2 = 0.73) best predicted timing of egg-laying in European Starlings (Sturnus vulgaris). A "mid-winter" temperature window was also strongly correlated with laying date (r2 = 0.58) but there was no support for an influence of short-term temperatures immediately before egg-laying. We assessed the relationship between ambient temperature and timing of egg-laying using three complimentary approaches: a) an "unconstrained", exploratory analysis, b) a traditional sliding window approach, and c) specific, biologically-informed temperature windows. Our results contrast markedly with the widely held view, in the avian breeding phenology literature, that immediate, pre-breeding, temperatures best predict short-term variation in laying because they allow birds to fine-tune timing of breeding to local conditions around the time of egg-laying. This means that mechanisms that allow integration of long-term temperature information must exist in birds – perhaps most parsimoniously involving direct effects of temperature on growth of the bird's ectothermic insect prey– even though these are currently poorly characterized.
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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".