Estimating interspecific economic risk of bird strikes with aircraft
Bibliographic record
Abstract
ABSTRACT The International Civil Aviation Organization promotes prioritization of wildlife management on airports, among other safety issues, by emphasizing the risk of wildlife–aircraft collisions (strikes). In its basic form, strike risk comprises a frequency component (i.e., how often strikes occur) and a severity component reflecting the cost of the incident. However, there is no widely accepted formula for estimating strike risk. Our goal was to develop a probabilistic risk metric that is adaptable for airports to use. Our specific objectives were to 1) update species‐specific, relative hazard scores (i.e., the likelihood of aircraft damage or effect on flight when strikes occur) using recent U.S. Federal Aviation Administration (FAA) wildlife strike data (2010–2015); 2) develop 4 a priori risk models, reflecting species‐specific strike data and updated relative hazard scores; 3) test these models against independent data (monetary costs associated with strikes); and 4) apply our best model to strike data from 4 large, FAA‐certificated airports to illustrate its application at the local level. Our best‐fitting risk model included an independent variable that was an interaction of quadratic transformed relative hazard score and number of wildlife strikes ( r 2 = 0.74). Top species in terms of estimated risk nationally were red‐tailed hawk ( Buteo jamaicensis ), Canada goose ( Branta canadensis ), turkey vulture ( Cathartes aura ), rock pigeon ( Columba livia ), and mourning dove ( Zenaida macroura ). We found substantial overlap among the top 5 riskiest species locally across 3 of 4 airports considered, illustrating the degree of site‐specific differences that affect risk. Strike risk is dynamic; therefore, future work on risk estimation should allow for model adjustment to reflect ongoing wildlife management actions at airports that could influence future strike risk. Published 2018. 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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".