Do lighting conditions influence bird–window collisions?
Bibliographic record
Abstract
Bird–window collisions account for approximately one billion bird deaths annually in North America. Highly reflective or mirrored glass is associated with increased collision risk, but little is known about whether the reflection caused by differential lighting of otherwise clear glass influences the risk of window collisions. We aimed to determine whether reflection from a clear window influences daytime collision risk by manipulating the lighting conditions on exterior and interior window surfaces. In a flight tunnel, we flew domesticated Zebra Finches (<em>Taeniopygia guttata</em>) toward windows manipulated to be of higher or lower reflection and recorded collision risk and flight velocity using three-dimensional videography. We predicted that the risk of collision would be greater when windows were manipulated to be more reflective. We found no support for this prediction. In contrast, we found that collision risk decreased in the presence of a stronger reflection during bright, midday exterior-lighting conditions. We suggest that the influence of window reflection on daytime window collisions is more complex than often assumed and might involve previously unaccounted properties of light, such as the polarity of light. Lastly, we recommend directions for future collision research and non-invasive mitigation strategies which involve the manipulation of interior lighting throughout the day.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.008 | 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".