Monitoring the Frequency and Severity of Bird Collisions : An Analysis of Factors Influencing the UBC Chan Centre and Wall Institute
Bibliographic record
Abstract
Bird-building collisions are the leading cause in avian mortality, accounting annually for millions of mortal collisions in Canada. Because of the important contribution birds have to the ecosystem, it is necessary to understand how and why these collisions occur. Using modified methodology, this monitoring project aims to determine the frequency and severity of bird collisions at the Chan Centre and Wall Institute located on UBC’s Vancouver campus. Over an eight week period between February 5th 2024 and March 29th 2024, building facades were monitored frequently and collision evidence recorded. It was found that 30 total collisions occurred over this period, with significantly higher frequency located at facades with continuous glass and high proximity to vegetation. The high frequency and severity of bird collisions located at the Chan Centre and Wall Institute during this period is evidence that proactive solutions are needed in order to reduce collision occurrences. As this is the first year the Chan Centre or the Wall Institute are being monitored, future research is needed to identify the frequency of collisions and the priority areas for migration. 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.”
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".