Complex relationships between climate and reproduction in a resident montane bird
Bibliographic record
Abstract
Animals use climate-related environmental cues to fine-tune breeding timing and investment to match peak food availability. In birds, spring temperature is a commonly documented cue used to initiate breeding, but with global climate change, organisms are experiencing both directional changes in ambient temperatures and extreme year-to-year precipitation fluctuations. Montane environments exhibit complex climate patterns where temperatures and precipitation change along elevational gradients, and where exacerbated annual variation in precipitation has resulted in extreme swings between heavy snow and drought. We used 10 years of data to investigate how annual variation in climatic conditions is associated with differences in breeding phenology and reproductive performance in resident mountain chickadees ( Poecile gambeli ) at two elevations in the northern Sierra Nevada mountains, USA. Variation in spring temperature was not associated with differences in breeding phenology across elevations in our system. Greater snow accumulation was associated with later breeding initiation at high, but not low, elevation. Brood size was reduced under drought, but only at low elevation. Our data suggest complex relationships between climate and avian reproduction and point to autumn climate as important for reproductive performance, likely via its effect on phenology and abundance of invertebrates.
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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".