Combined effects of migration distance, foraging method vegetation density, and population density on wing shapes of boreal songbirds
Bibliographic record
Abstract
Abstract In birds, migration distance is known to influence morphological attributes that influence flight performance, especially wing shape. However, wing shape is under the likely influence of less documented factors such as foraging method, vegetation density and isolation of individuals and populations. To better understand factors leading to interspecific differences in wing shape, we measured the pointedness of wings (Kipp’s distance) of 1017 live birds of 22 species in an eastern Canadian boreal forest. We modeled wing pointedness as a function of migration distances from eBird records, foraging, habitat, and population density data from Birds of North America monographs. Long-distance migrants and species living in low-density vegetation had more pointed wings than shorter-distance migrants and dense-vegetation dwellers, in accordance to our predictions. After accounting for vegetation density and migration distance, we found no link between the extent of aerial foraging or mean breeding population density, an indicator of isolation, and wing pointedness. Those results are consistent with a tradeoff between sustained flight efficiency and maneuverability, but suggest that interspecific variation in wing shape due specifically to foraging method or habitat isolation is nonexistent or obscured by other factors.
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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 | 0.001 |
| 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 | 0.001 | 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".