The influence of morphological variation on migration performance in a trans-hemispheric migratory songbird
Bibliographic record
Abstract
Abstract For long-distance migratory songbirds, morphological traits such as longer wings and a smaller body size are predicted to increase migration efficiency. Due to previous limitations in our ability to track the long-distance journeys of small-bodied birds, the relationship between morphology and start-to-finish migration performance has never been fully tested in free-living songbirds. Using direct-tracking data obtained from light-level geolocators, we examined the effects of morphological factors (wing and body size) on spring and fall migration performance (flight speed, duration of stopovers, total stopovers taken) of a widely distributed, trans-hemispheric migratory songbird, the purple martin (Progne subis) (n = 120). We found that smaller-bodied birds spent fewer days at stopovers along fall migration, but larger-bodied birds spent fewer days at stopover and took fewer stopovers during spring migration. More of the variation in fall migration performance was explained by morphology, as compared to spring migration, possibly indicating a larger influence of environmental conditions on spring performance. Overall, our results partially support long-standing and previously untested predictions regarding the influence of intrinsic factors on migration performance. Future research should examine the influence of environmental variation on migration performance as well as additional morphological traits that may contribute to migration performance.
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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".