Could avian radar have prevented US Airways Flight 1549’s bird strike?
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
The heroic ditching in the Hudsn River of US Airay’s Flight 1549 following multiple bird strikes with Canada geese has increased public awareness of bird aircraft strike hazards (BASH); and has focused attention on new tools such as avian radar to help further improve aviation safety. Reports in the media have suggested that had avian radars been deployed at LaGuardia, this bird strike could have been avoided. Indeed, there is mounting evidence supporting existing avian radar’s ability to provide wildlife control and air operations personnel with greatly improved bird situational awareness which can be used to reduce bird hazards around airports for improved safety. But can avian radar provide pilots with the ability to sense and avoid specific bird hazards? The question requires careful consideration and is the subject of this paper. Using the Hudson incident as a case study, this paper examines the coverage and location accuracy needed if bird warnings to pilots are to be acted upon, followed by a look at the ability of today’s avian radars to provide these.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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