Bibliographic record
Abstract
North American Canada goose (Branta canadensis) populations continue to increase, causing potentially greater hazard to aviation. There is greater interest by biologist and aviation interests in monitoring the status of these populations because of the increasing number of Canada goose strikes to aircraft. Waterfowl in North America are managed in four administrative flyways – the Atlantic, Mississippi, Central, and Pacific. Goose numbers in these flyways are based on mid-winter or breeding period counts. The Canada goose count for North America in 2000 was 5,728,000—61% were the large resident geese. The resident component of the population has increased more than 3-fold from 1990-2000. Reported Canada goose strikes on aircraft have increased during recent years. For the years 1990-2001, Canada geese were identified in 61% of all goose strikes (606 of 985) reported to the FAA. Also, during the same reporting period, geese caused engine damage in 139 of the 985 strikes. Canada geese damaged 61% of the engines (85 of 139). The numbers of operating commercial jet aircraft and scheduled departures by airlines increase yearly. The higher number of Canada goose strikes probably is due to a greater awareness of the hazard and better reporting of strikes, and to the exposure of more commercial aircraft to increasing Canada goose populations. Aggressive integrated Canada goose management programs should continue or be undertaken to reduce this hazard.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".