Lights tuned to the avian eye result in early detection and escape from an approaching aircraft
Bibliographic record
Abstract
Collisions between birds and aircraft are a global problem. We identified different behavioral parameters affecting the probability of escape to an approaching aircraft, which is a function of the probability that the animal initiates an escape response (probability of reaction) and the probability of having enough time to escape (probability of sufficient time). Lights of high chromatic contrast tuned to the avian eye have been proposed as a solution to mitigate collisions. We approached Canada geese with a drone to estimate how aircraft lighting and changes in altitude, mimicking the flight phase where most strikes occur, affect parameters associated with the probability of escape. Onboard lights increased parameters associated with the probability of reaction at farther distances by promoting longer detection distances, which enabled the animal to initiate each stage of its escape response sooner leading to longer flight initiation distances irrespective of altitude changes. Additionally, onboard lights increased parameters associated with the probability of sufficient time where longer detection distances allowed animals to escape away from (as opposed to towards) the approaching drone. Our findings have implications for the development of light technology to deter birds away from approaching vehicles, and other anthropogenic structures (wind turbines, solar facilities).
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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".