Context-dependent Changes in the Weighting of Environmental Cues That Initiate Breeding in a Temperate Passerine, the Corsican Blue Tit (<i>Cyanistes caeruleus</i>)
Bibliographic record
Abstract
Birds use local environmental cues to fine-tune the timing of egg laying such that the nestling period normally coincides with the local peak in food availability. Ambient temperature, vegetation phenology, and insect phenology are often considered the most likely cues, but no previous studies have explicitly compared and partitioned their relative effects. We used confirmatory path analyses and a long-term study of Blue Tits (Cyanistes caeruleus) to identify and measure the relative weighting of the causal paths that link laying date to spring phenology and temperature in deciduous and evergreen oak forests on Corsica. Path analysis showed that the effects of temperature and vegetation phenology vary between forest types and season. In deciduous oak forest, where females lay eggs early in spring, phenology of vegetation or insects sets the laying date. In evergreen oak forest, where breeding occurs later in the season, females shift from a predominantly phenology-based cue system to a predominantly temperature-based cue system. This plasticity in the decision process allows birds to minimize the risk of mismatching breeding date with the optimal time window and may be critical in allowing birds to track human-induced environmental change.
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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".