<b>Assessing the Behavioral Responses of Canada Geese to Lights Tuned to their Visual Systems: Implications for Bird-Aircraft Collisions</b>
Bibliographic record
Abstract
Collisions between birds and aircraft are a source of avian mortality, cause substantial economic damage, and are a major safety hazard for the global aviation industry. The theoretical frameworks attempting to explain animal behavioral responses to approaching high-speed vehicles are scarce. Some potential theoretical frameworks to apply have been developed for responses to natural predators. While both share similar principles, such as detection always precedes escape and escape responses require time to both initiate an enact (Chapter 1), antipredator theory is severely limited in the ability to predict behavioral responses to high-speed vehicles primarily because of the little time animals have to enact a response after such detection occurs (Chapter 1). A potential solution to mitigate the negative effects of vehicle approach speed is the use of lighting onboard a vehicle to increase the distance a threat is detected providing the animal with more time to escape. We assessed the behavioral responses of Canada geese (Branta canadensis), a frequently struck species capable of causing substantial damage, to lights of high chromatic contrast specific to their visual physiology. We first evaluated behavioral responses of different wavelengths of high chromatic contrast and identified that blue wavelengths tended to elicit avoidance responses over repeated exposures (Chapter 2). Subsequently, we assessed how the presence of high chromatic contrast blue lights influenced both goose detection and escape responses to an approaching aircraft (i.e., a drone or UAS) (Chapter 3). We found that lights of high chromatic contrast indeed resulted in early detection, but that also translated to earlier escape (Chapter 3). Lastly, we developed a model to answer a large outstanding question relevant to the use of lights to mitigate animal vehicle collisions: the distance at which a bird detects a light source (Chapter 4). Generally, our results suggest that the distance the avian visual system detects a light source increases as photon intensity increases but the magnitude of the increase heavily depends on light wavelength, the prevailing visibility, and ambient light conditions. Our research results are particularly relevant for the airline industry as well as low-altitude aircraft technology coming in the coming years.
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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 | 0.000 |
| 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".