Associations between sex, age and species-specific climate sensitivity in migration
Bibliographic record
Abstract
Abstract Weather often plays a key role in migration timing, and temporal shifts over the past century have been heavily researched and linked to climate change. Much research is however limited by the use of arbitrary time periods during which weather is thought to most influence migration. Here, we compare the classic fixed window method to a novel sliding window approach created to determine periods of temperature sensitivity among organisms, in this case on the migration phenology of nineteen passerine species banded at the McGill Bird Observatory in Montréal, Québec, from 2005 to 2015. We found overall shorter temperature sensitivity windows in the spring than the fall migration and deemed the nonarbitrarily chosen periods of temperature sensitivity to be more useful than the classic fixed window method when used with caution. We also found significant variation in migration timing of 11 species, as well as more cases of male birds arriving in spring prior to females than the reverse. More males departed in fall before females as well. Similarly, on average, older birds arrived in spring ahead of younger individuals and departed prior to younger in the fall.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".