Towards building a speciesspecific risk model for mammal-aircraft strikes
Bibliographic record
Abstract
Wildlife strikes are a significant issue in the aviation industry, especially strikes with medium- to large-sized mammals, which pose a high risk of damage to aircraft and human safety. Despite the identified threat that mammals can pose to aircraft, few works have been published on ways to rank and predict the risk of mammalian species to aircraft. This study used remote camera trap data collected from an array of wildlife camera traps at the Prince George International Airport (YXS), Prince George, British Columbia, Canada, to calculate strike risk for various species of mammals involved in runway incidents between January 2012 and December 2018. Carnivores such as red foxes and coyotes were found to be the highest risk mammal species at YXS, but foxes were found airside infrequently compared to coyotes. Binary logistic regression modelling was used in an attempt to predict variables leading to runway incidents with coyotes at YXS. The highest supported logistic regression model predicting coyote incidents included the variables ‘weekday’, ‘month’ and ‘season’. Although data from camera traps did not help to predict incidents, trend data collected from camera traps mirrored coyote incident data, suggesting that camera traps are useful for capturing times of the day and seasons of the year in which coyotes are active at the airport. Suggestions are provided as to how cameras might be used to track the movement of animals more accurately and what other data could be useful in helping to build risk assessments and models to predict aircraft incidents with mammals of interest.
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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 | 0.000 |
| 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".