Bibliographic record
Abstract
The reporting of wildlife collisions with aircraft in almost all places, worldwide, is voluntary. As a result data with which to design, manufacture and operate aircraft to mitigate this hazard is poor. Voluntary reporting of strikes has resulted in data collection rates in the USA of around 20%, and only about 9% of the reported strikes contain complete data on bird species. Aviation manufacturers also agree that collection of strike data is difficult, incomplete and without an industry best practice. Air carriers, when research is done, are amazed to find that strike rates may be eight times higher than their normal collection methods demonstrate. The USA safety agency, NTSB, has recommended that wildlife strike reporting be mandatory. Reporting methods and databases, in the USA and Canada, are already in place. ICAO maintains a strike database for states worldwide, but participation is poor. While the cost of mandatory reporting is often cited as a reason for not implementing mandatory reporting, the cost of not reporting is higher. Since 1995, over 130 people, worldwide, have lost their lives to collisions between wildlife and aircraft. Air carriers lose US$1.2 billion to bird strikes each year. If carriers reduced this loss by only 25%, the savings to carriers each year would be US$300 million. Without adequate data, neither the location, nor the frequency, nor the type of problem wildlife can be adequately identified. Neither adequate aircraft design nor operating techniques can be developed without data. Voluntary reporting has not worked: it is time for mandatory reporting of data.
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 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 | 0.100 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".