Using dietary analyses to reduce therisk of wildlife–aircraft collisions
Bibliographic record
Abstract
Wildlife-Aircraft collisions (wildlife strikes) pose a serious safety risk to aircraft and cost civil aviation >$614 million annually in the United States (Dale 2009, Dolbeer et al. 2009). Over 89,700 wildlife strikes with civil aircraft were reported to the U.S. Federal Aviation Administration (FAA) during 1990 to 2008 (Dolbeer et al. 2009). Aircraft collisions with birds accounted for 97% of the reported strikes (Dolbeer et al. 2009). Gulls (Larus spp.), waterfowl, such as Canada geese (Branta canadensis), raptors (hawks and owls), blackbirds, and European starlings (Sturnus vulgaris) are the species of most concern at airports (Dolbeer et al. 2000, Dolbeer and Wright 2009). Analyzing information from the FAA’s National Wildlife Strike Database regarding wildlife strikes with civil aircraft, Dolbeer (2006) found that 74% of all wildlife strikes were at altitudes of ≤125 m above ground level (AGL) and suggested that most wildlife strikes occur within the airport environment. Sound management techniques that reduce bird numbers in and around airports are therefore critical for safe airport operations. Large-scale killing of birds to solve conflicts oft en is undesirable or impractical (Dolbeer 1986, Dolbeer et al. 1997). Nonlethal frightening techniques to keep birds away from airports are available (Marsh et al. 1991, Cleary 1994), but they can be cost-prohibitive or only temporarily effective (Dolbeer et al. 1995, Washburn et al. 2006, Baxter and Allan 2008). Habitat management within airport environments is the most important long-term component of an integrated approach to reduce the use of airfields by birds and mammals that pose hazards to aviation (Transport Canada 1994, Washburn and Seamans 2004, Cleary and Dolbeer 2005, Washburn et al. 2007).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".