Identification of off airport interspecific avian hazards to aircraft
Bibliographic record
Abstract
ABSTRACT Understanding relative hazards of wildlife to aircraft is important for developing effective management programs that can minimize hazards from wildlife strikes. Although interspecific differences in hazard level of birds and mammals on airport properties are described, no studies have quantified hazard level of bird species or identified factors that influence hazard level when birds are struck beyond airport boundaries (e.g., during aircraft climb or approach). We used Federal Aviation Administration National Wildlife Strike Database records from 1990 through 31 May 2014 to identify bird species involved most often in collisions with aircraft beyond airport boundaries in the United States and to quantify the interspecific hazard level of those birds. We also investigated whether body mass, group size (single or multiple birds), region (Flyway), and season influenced the likelihood of aircraft damage and substantial damage when strikes occurred using binary logistic regression analysis. Canada geese ( Branta canadensis ; n = 327), turkey vultures ( Cathartes aura ; 217), American robins ( Turdus migratorius ; 119), and mallards ( Anas platyrhynchos ; 107) were struck most often by aircraft beyond airport boundaries. Waterbirds (cormorants, ducks, geese, and to a lesser extent, gulls) and raptors (including vultures) were most likely to cause damage or substantial damage to aircraft when strikes occurred. Body mass was an important predictor of hazard level; group size, region, and season had lesser effects on hazard level. Management strategies to reduce bird strikes with aircraft beyond airport properties should be active throughout the year and prioritize waterbirds and raptors. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
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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) | 0.001 | 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 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".