Tracking Canada Geese Near Airports: Using Spatial Data to Better Inform Management
Bibliographic record
Abstract
The adaptation of birds to urban environments has created direct hazards to air transportation with the potential for catastrophic incidents. Bird–aircraft collisions involving Canada geese (Branta canadensis; goose) pose greater risks to aircraft than many bird species due to their size and flocking behavior. However, information on factors driving movements of geese near airports and within aircraft arrival/departure areas for application to management are limited. To address this need, we deployed 31 neck collar-mounted global positioning system transmitters on Canada geese near Midway International Airport in Chicago, Illinois, USA during November 2015 to February 2016. We used the movement data obtained to model environmental and behavioral factors influencing the intersection of goose movements (i.e., transition from 1 location to another) with air operations areas (i.e., aircraft flight paths). Of 3,008 goose movements recorded, 821 intersected a 3-km buffer around the airport representing U.S. Federal Aviation Administration recommended distances from wildlife attractants, and 399 intersected flight paths for approaching and landing aircraft. The effects of weather (i.e., snow cover, temperature, wind speed) on the probability of geese flying varied with different air operation areas while certain habitat resources greatly increased the probability of intersection. For example, the juxtaposition of foraging (railyards with spilled grain) and loafing areas (rooftops) near the airport led to a higher probability of movements intersecting important air operations areas. The average altitude of flying geese was 29.8 m above the ground, resulting in the greatest risk of collision being within 0.5 km of the end of runways. We suggest airport goose collision mitigation management actions, such as reducing habitat resources near the airport and using focused nonlethal harassment or physical modifications, when guided by animal movement data, may further mitigate bird-strike risks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".