Factors influencing the annual risk of bird–window collisions at residential structures in Alberta, Canada
Bibliographic record
Abstract
Context Increasingly, ornithologists are being asked to identify major sources of avian mortality so as to identify conservation priorities. Aims Considerable evidence suggests that windows of office towers are a lethal hazard for migrating birds. The factors influencing the risk of bird–window collisions in residential settings are not understood as well. Methods Citizen scientists were requested to participate in an online survey that asked about characteristics concerning their homes and yards, general demographic information about participants, and whether they had observed evidence of bird–window collisions at their home. Key results We found that 39.0% of 1458 participants observed a bird–window collision in the previous year. The mean number of reported collisions was 1.7 ± 4.6 per residence per year, with 38% of collisions resulting in a mortality. Conclusions Collisions were not random, with the highest collision and mortality rates at rural residences, with bird feeders > rural residences without feeders > urban residences with feeders > urban residences without feeders > apartments. At urban houses, the age of neighbourhood was a significant predictor of collision rates, with newer neighbourhoods reporting fewer collisions than older neighbourhoods. Most people remembered collisions occurring in the summer months. Implications Our results are consistent with past research, suggesting that window collisions with residential homes are an important source of mortality for birds. However, we found large variation in the frequency of collisions at different types of residences. Proper stratification of residence type is crucial to getting accurate estimates of bird–window collisions when scaling local data into larger-scale mortality estimates.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".