Does wind speed and direction influence timing and route of a trans-hemispheric migratory songbird (purple martin) at a migration barrier?
Bibliographic record
Abstract
Abstract The influence of weather on the departure decisions and routes of migratory birds can now be further investigated with the use of direct tracking methods. We tested hypotheses for migration departure decisions and flight trajectories by determining the influence of wind speed and direction at the Yucatan peninsula in spring on departure date, migratory route, and longitude of arrival at the northern Gulf coast of a trans-hemispheric migratory songbird, purple martin (Progne subis). Birds were equipped with geolocators at their breeding colony and 36 were recaptured upon return after spring migration. While southerly tailwinds with low wind speeds prevailed at the Yucatan during the period of passage, we found that daily wind speed and direction were still important predictors of departure date. However, wind conditions at departure did not predict longitude of arrival at the US gulf coast after crossing the gulf. Birds appeared to favour the shortest distance across the Gulf of Mexico, aided by consistent tailwinds, but may have corrected for wind drift so as to land at a longitude near 88°, reflecting the shortest distance across from the Yucatan staging areas. Considering their use prior to departure, high quality roost sites at the Yucatan peninsula would be important conservation targets for this declining aerial insectivore.
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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".