Characterising Bird-Window Collisions from an Avian Visual Perspective
Bibliographic record
Abstract
Collisions with architectural glazing on buildings are a major anthropogenic source of bird mortality. Bird collisions can be prevented by applying bird safe design principles in building construction and renovations. Modifications to the appearance of glazing, such as using applications of visual markers on glass, can provide signals to alert birds to presence of an obstacle. To optimize visual markers for mitigating collisions with buildings, bird safe design must consider visual perception and behaviour of birds in the moments leading up to detection and avoidance. However, avian visual perception of glass, a unique and highly dynamic category of materials, remains poorly described in the literature. Defining relationships between avian and human visual perception, glazing and environmental variables such as lighting can support more effective real-world architectural applications of emerging technologies tested under controlled experimental conditions. This thesis examined environmental variables associated with the risk of bird collisions with glass on institutional campus buildings in London, Ontario, Canada as well as the efficacy of mitigation techniques using visual markers. To develop the methods, I reviewed assumptions and constraints associated with existing glass testing, such as experiments in the field and using flight arenas. I discussed approaches to improving the interpretation and generalisability of behavioural data. I then applied a combination of novel experimental and passive observational methods using building surveys, video recording and flight arenas to examine understudied aspects of collisions, yielding insights into effects of lighting conditions, flight kinematics, and glazing characteristics on outcomes of collisions. I found that collision risk increases with reflectivity of glass and decreases when surfaces use high contrast markings. When accounting for other factors, building façades with the most reflective glazing were associated with the greatest number of collisions. I found birds tested in flight experiments avoided markers applied on glass and transparent film. I gathered evidence that avoidance was affected by environmental factors such as lighting and the background. Translating knowledge from other academic disciplines and sectors, I provided recommendations for prioritizing further research, policy and program development to address social dimensions of this rapidly evolving challenge for the conservation of migratory birds.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".