Wildlife in Airport EnvironmentsPreventing Animal-Aircraft Collisionsthrough Science-Based Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
On 15 January 2009, the world learned --- in dramatic fashion --- that wildlife pose serious hazards to aircraft. On that day, US Airways Flight 1549, an Airbus 320 carrying 155 people, made an emergency landing in the Hudson River in New York City after ingesting Canada geese (Branta canadensis) into both engines at an altitude of ~2,900 feet (880 m) following takeoff from LaGuardia Airport (Marra et al. 2009, National Transportation Safety Board 2010). Historically, most people had never considered the extent of hazards posed to aircraft by birds and other wildlife. After all, how can birds, which generally weigh a few kilograms at most, bring down an airliner? Don't they just bounce off or get shredded by the powerful engines?
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it