Evaluating bird-window collision patterns, species vulnerability, and a mitigation approach
Bibliographic record
Abstract
Bird populations are declining at an unprecedented rate worldwide. In North America, 57% of all bird species have experienced population declines in the last 50 years. Collisions with building windows are one of the top avian mortality sources contributing to these declines, and in North America alone, potentially over one billion birds die annually from window collisions. Despite the importance of window collisions to global bird conservation, many substantial gaps remain in the window collision literature. These gaps include a lack of rigorous field testing of products designed to mitigate collisions, and a scarcity of studies evaluating collision patterns at broad spatiotemporal scales. To address these knowledge gaps, we conducted two separate studies. Firstly, we conducted a before-after control-impact (BACI) study of the effectiveness of a mitigation product at the Oklahoma State University campus—specifically, monitoring of window collisions both before and after a treatment of Feather Friendly® 5x5 cm white dot markers. Secondly, we analyzed continental-scale taxonomic and spatiotemporal patterns of window collisions using bird band recovery data from the North American Bird Banding Program (NABBP) dataset and estimated vulnerability of individual species to window collisions. In relation to the first study, we found a 67.2-71.6% reduction in collisions at glass façades treated with the window markers, in contrast to a 15.7% increase in collisions at untreated façades during the same period. This result emphasizes the promise of window markers as a collision mitigation technique and offers a framework for a rigorous study design that can be used for future studies on similar products. For the second study, we found that the NABBP dataset included collision records for 298 species and 51 families, encompassing the entire continental United States and southern Canada. Many of the most vulnerable species in the dataset were from families currently underrepresented in the window collision literature, including hawks, falcons, owls, and finches. This underscores the need for demographic modeling studies to determine if species in these families are experiencing population-level effects from window collisions. Collectively, we offer valuable insights into the effectiveness of collision mitigation products and broad-scale species vulnerability to window collisions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".