Increasing Trend of Damaging Bird Strikes with Aircraft Outside the Airport Boundary: Implications for Mitigation Measures
Why this work is in the frame
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Bibliographic record
Abstract
A basic tenet of programs to mitigate the risks of bird strikes with aircraft has been to focus management efforts at airports because various historical analyses of bird-strike data for civil aviation have indicated the majority of strikes occur in this environment during take-off and landing at (AGL). However, a trend analysis of birdstrike data involving commercial air carriers from the U.S. National Wildlife Strike Database for Civil Aviation, 1990 to 2009, indicates that this tenet should be revised. The percentage of all strikes that occurred at >500 feet AGL increased significantly from about 25% in 1990 to 30% in 2009. The percentage of all damaging strikes that occurred at >500 feet increased at a greater rate, from about 37% in the early 1990s to 45% during 2005 to 2009. I also examined trends in strike rates (strikes/1 million commercial aircraft movements) for strikes occurring at < and >500 feet. From 1990 to 2009, the damaging strike rate at >500 feet increased from about 2.5 to 4.0, whereas the damaging strike rate for strikes at (Branta canadensis), the most frequently struck bird species with a body mass >1.8 kg, showed a pattern similar to that for all species. I conclude that mitigation efforts incrementally implemented at airports in the United States during the past 20 years have resulted in a reduction of damaging strikes in the airport environment. This reduction in strikes has occurred in spite of increases in populations of Canada geese and many other species hazardous to aircraft. However, these successful mitigation efforts, which must be sustained, have done little to reduce strikes outside the airport. Increased efforts now are needed to eliminate bird attractants within 5 miles of airports, to further develop bird-detecting radar and bird-migration forecasting, and to research avian sensory perception to enhance aircraft detection and avoidance by birds.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it