Relative breeding timing and reproductive success of a resident montane bird species
Bibliographic record
Abstract
Wild populations appear to synchronize their reproductive phenology based on numerous environmental and ecological factors, yet there is still individual variation in the timing of reproduction within populations and such variation may be associated with fitness consequences. For example, many studies have documented a seasonal decline in reproductive fitness, but breeding timing may have varying consequences across different environments. Using 11 years of data, we investigated the relationship between relative breeding timing and reproductive success in resident mountain chickadees (Poecile gambeli) across two elevational bands in the Sierra Nevada mountains, USA. Chickadees that synchronized breeding with the majority of the population ("peak" of breeding) did not have the highest breeding success; instead, birds that bred early performed best at high elevation, while at low elevation early and peak nests performed similarly. At both elevations, late nests consistently performed the worst. Overall, breeding success decreased with increasing relative timing at both high and low elevations, but the relationship between breeding success and timing differed among years. Our results suggest that in mountain chickadees, earlier breeding is associated with higher reproductive success, especially at high elevations, while late breeding is consistently associated with lower reproductive success at both elevations.
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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 | 0.001 | 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".