Light wavelength and pulsing frequency affect avoidance responses of Canada geese
Bibliographic record
Abstract
Collisions between birds and aircraft cause bird mortality, economic damage, and aviation safety hazards. One proposed solution to increasing the distance at which birds detect and move away from an approaching aircraft, ultimately mitigating the probability of collision, is through onboard lighting systems. Lights in vehicles have been shown to lead to earlier reactions in some bird species but they could also generate attraction, potentially increasing the probability of collision. Using information on the visual system of the Canada goose ( Branta canadensis ), we developed light stimuli of high chromatic contrast to their eyes. We then conducted a controlled behavioral experiment ( i.e. , single-choice test) to assess the avoidance or attraction responses of Canada geese to LED lights of different wavelengths (blue, 483 nm; red, 631 nm) and pulsing frequencies (steady, pulsing at 2 Hz). Overall, Canada geese tended to avoid the blue light and move towards the red light; however, these responses depended heavily on light exposure order. At the beginning of the experiment, geese tended to avoid the red light. After further exposure the birds developed an attraction to the red light, consistent with the mere exposure effect. The response to the blue light generally followed a U-shape relationship (avoidance, attraction, avoidance) with increasing number of exposures, again consistent with the mere exposure effect, but followed by the satiation effect. Lights pulsing at 2 Hz enhanced avoidance responses under high ambient light conditions; whereas steady lights enhanced avoidance responses under dim ambient light conditions. Our results have implications for the design of lighting systems aimed at mitigating collisions between birds and human objects. LED lights in the blue portion of the spectrum are good candidates for deterrents and lights in the red portion of the spectrum may be counterproductive given the attraction effects with increasing exposure. Additionally, consideration should be given to systems that automatically modify pulsing of the light depending on ambient light intensity to enhance avoidance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".