Bird-Window Collision MItigation at USU's C&SS Building, Brigham City, Utah
Bibliographic record
Abstract
Bird-window collisions are often not thought about as if they are a major problem to bird populations worldwide. This is not the case as bird populations are threatened by these collisions. In the United States alone it is estimated that 97.6 - 975.6 million birds fatally collide with human-made windows annually, and another 16 to 42 million collide in Canada per year. Our focus is to investigate a possible window collision problem and explore different mitigation efforts to prevent these collisions at the USU-Brigham City campus (Brigham City, Utah, 84302). We hope to determine how many fatal bird-window collisions are occurring on an annual basis. We are completing a daily census and already have data from previous years; however, those data cover only August to December, so our census will run for 12 months. Once enough information and data are collected, we plan to determine which types of mitigation efforts work best given the climate, location and behaviors of the local bird populations. We predict that large open windows with foliage nearby to be hotspots given previous data. These hotspots will be important locations to test out different mitigation efforts to be able to determine what works best to mitigate fatal bird window-collisions. When this is complete we plan to test the effects of our mitigation efforts by continuing the census. We also plan to educate the community on how to prevent these collisions at their own homes as more than 50 percent of collisions are on residential properties.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".