Invisible to visible : A field study investigating dirty windows and bird-friendly artwork as mitigation strategies against bird-window collisions
Bibliographic record
Abstract
Estimates suggest that 16 to 43 million bird deaths occur annually due to collisions with buildings in Canada. Buildings on the University of British Columbia Vancouver campus may contribute up to 10,000 bird deaths each year. The reflective and transparent properties of glass, combined with birds having poor spatial acuity in the direction of motion, make it challenging for birds to perceive glass as a solid barrier, resulting in bird-window collisions. This study investigates the effect of two mitigation strategies employed by the UBC Botanical Garden: reducing window washing frequency, which allows dirt to accumulate on windows and the installation of bird-friendly artwork. A comparison of collision frequency over an 8-week monitoring period in late winter and early spring of 2021 and 2022 is used to investigate the effectiveness of these strategies. A 97% decrease in collision evidence was reported from 2021 to 2022, suggesting that dirty windows and bird-friendly artwork are effective at reducing birdwindow collisions. We recommend that both strategies should be implemented at other buildings on the UBC campus and suggested to businesses and homeowners to reduce the negative impact windows have on bird populations. Further studies should be conducted on the effect of dirty windows, as limited research directly investigates this strategy. Additionally, a comparison between the effectiveness of dirty windows, bird-friendly artwork, and other mitigation strategies (such as decals or ultraviolet film application) should be conducted to determine which offers the highest level of protection against bird-window collisions. Future research should also investigate social aspects of bird-window collisions, including public perception and awareness, in order to improve mitigation strategies and better understand barriers to their implementation. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.003 | 0.001 |
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".