Wildlife risk to aviation: a multi-scale\nissue requires a multi-scale solution
Bibliographic record
Abstract
Aircraft collisions with birds and other wildlife (wildlife strikes) pose increasing safety and financial concerns to the aviation industry worldwide. Recent events such as the ditching of US Airways Flight 1549 in the Hudson River have renewed public interest in risks to aircraft posed by wildlife (Marra et al. 2009). However, wildlife biologists and aviation personnel have been aware of these issues for decades (Solman 1973, Blokpoel 1976). Since the inception of the Federal Aviation Administration’s (FAA) National Wildlife Strike Database in 1990, 99,411 reported wildlife strikes to airplanes have resulted in at least $1.2 billion annually in losses (direct and indirect) to civil aviation worldwide and >$625 million annually in the United States, as well as >200 human lives lost (Allan 2002, Dolbeer et al. 2010).\nWildlife-strike mitigation at airports involves reducing the likelihood that a strike occurs and reducing the level of damage if a strike does happen. Historically, wildlife management at airports has occurred at small spatial scales relative to overall animal space use. Wildlife damage management strategies (e.g., harassment and deterrents) usually occur within the confines of airport property. However, the effectiveness of these techniques depends in part on the surrounding landscape and ecology of species involved. For example, the Cessna Citation 1 crash in Oklahoma in 2008 that killed 5 people was caused by American white pelicans (Pelecanus erythrorhynchos) likely flying to or from a lake <2 km from the crash site (Dove et al. 2009, National Transportation Safety Board 2009). York et al. (2000) reported that site-specific return rates of Canada geese (Branta canadensis) to a U.S. Air Force base after harassment were contingent on the distance from the airport to their resting site.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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; both teacher heads agree on what is shown here.
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".