The impacts of environmental variability and climate change on the migration and breeding phenology of a long-distance migratory songbird
Bibliographic record
Abstract
Climate change is advancing spring phenology but it remains unclear whether migratory birds are able to adjust their timing to match these changes. Purple martins (Progne subis) are long-distance migrants, and part of a functional taxon (aerial insectivores) that are undergoing population declines. My objectives were to: 1) determine if environmental variability during spring migration predicts individual timing of migration, and 2) determine if laying date is phenotypically plastic to spring temperatures. I found that spring migration phenology was not predicted by environmental factors and individual martins had repeatable spring migration timing. Laying date was earlier with warmer temperatures, fledgling numbers increased with earlier laying dates, and selection pressure for earlier breeding did not change with temperature. Overall, my results suggest that timing is constrained through much of the annual cycle, but purple martins can adjust to current climate conditions by varying their laying date with temperature during the breeding period.
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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 | 0.000 |
| 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) | 0.002 | 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".