Flight morphology and visual obstruction predict collision risk in birds
Bibliographic record
Abstract
ABSTRACT Collisions with buildings are a major source of mortality for wild birds, but these instantaneous events are difficult to observe. As a result, the mechanistic causes of collision mortality are poorly understood. Here, we evaluate whether sensory and biomechanical traits can explain why some species are more collision-prone than others. We first examined concordance of species vulnerability estimates across previous North American studies to determine whether these estimates are repeatable, and whether vulnerability is more similar among closely-related species. We found moderate concordance and phylogenetic signal, indicating that some bird species are consistently more collision-prone than others. We next tested whether morphological traits related to flight performance and sensory guidance explain these differences among species. Our comparative analysis shows that two traits primarily predict collision vulnerability within passerines: relative beak length and relative wing length. Small passerine species with relatively short wings and those with relatively long beaks are more collision-prone, suggesting that greater maneuverability and obstructed vision contribute to risk. Together, these findings can help inform mitigation strategies and predict which species will be most at risk in other regions.
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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 | 0.001 | 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) | 0.002 | 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".