Use of wildlife camera traps to aid in wildlife management planning at airports
Bibliographic record
Abstract
Wildlife incidents with aircraft cost airports and operators worldwide an average of US$1.28bn annually. In Canada, Airport Wildlife Management Plans (AWMPs) are designed to provide an outline of specific wildlife hazards at airports and recommend countermeasures to minimise strike risk. Wildlife incident reports are a key component in the development of such plans. Here, wildlife incident reports were compiled and compared to data collected using newly-installed digital wildlife camera trap technology at the Prince George International Airport. Seven camera traps were monitored for a total of 2,426 sampling days (9,228 camera days) between 2009 and 2016 and recorded a total of 3,046 animals within 16 different animal species/groups. Airport personnel recorded 4,640 animals and 23 different species/ groups during the same period. Camera traps recorded almost five times as many animals (n = 2,525) on days when no wildlife incident reports were filed than days when wildlife incident reports were filed (n = 521) and camera traps recorded no images. Z-test for proportions analyses indicated that birds (ie flocks) were more commonly observed and reported by airport personnel than were captured by camera traps, while mammals such as moose (Alces alces), black bears (Ursus americanus) and snowshoe hares (Lepus americanus) were more commonly recorded by camera traps. These findings suggest that data from camera traps can help in the development of more informed AWMPs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".