DEFINING AND REDUCING WILDLIFE HAZARDS TO AVIATION IN THE USA
Bibliographic record
Abstract
The U.S. Department of Agriculture’s National Wildlife Research Center (NWRC), through an interagency agreement with the U.S. Federal Aviation Administration (FAA), conducts a national research program to define and reduce bird and other wildlife hazards to aircraft. The goal is to provide the FAA and airports nationwide with a scientific foundation for policies and recommendations related to wildlife and aviation safety. Research tasks conducted by NWRC under the agreement include: 1) investigations of habitat management and land-use practices on and near airports to reduce bird activity; 2) development and evaluation of bird repellent and frightening methods for airports; 3) management and analysis of the National Wildlife Strike Database for civil aviation; and 4) development of publications, including a manual on wildlife hazard management, for use by airport operators nationwide. The research, coordinated by NWRC’s Sandusky, Ohio Field Station, has resulted in over 100 scientific publications since 1992. Recent highlights include 1) development of a wildlife strike database for civil aviation with about 28,000 strike reports, 1990-1999; 2) publication of a report, based on analyses of data in the bird strike database, which indicated wildlife collisions with aircraft cost U.S. civil aviation over $300 million/year, 1990-1998; 3) partnership with private industry to develop chemical repellents for use against Canada geese on airports; 4) development of management program at JFK International Airport, New York, that resulted in a 90% reduction in gull-aircraft collisions; and 5) publication of a comprehensive manual for airport personnel entitled “Wildlife Hazard Management at Airports”.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".