Population Trends of Resident and Migratory Canada Geese in Relation to Strikes with Civil Aircraft
Bibliographic record
Abstract
Canada geese (Branta canadensis) are of particular concern to aviation in the USA because of their large size, flocking behavior, attraction to airports for grazing, and, for the resident population, year-round presence in urban environments. We documented trends in resident and migrant Canada goose populations in North America from 1970 to 2012, and for 1990 to 2012 examined these trends in relation to trends in reported civil aircraft collisions (strikes) with Canada geese. The overall Canada goose population increased 4.5 fold from 1.26 million in 1970 to 5.69 million in 2012. Most of this overall increase was due to a 15.6-fold increase in the population of resident geese (from 0.25 to 3.85 million), especially during the 1990s when the population increased at a mean annual rate of 12.7%. From 2000 to 2012, the resident population has stabilized, fluctuating between 3.36 and 3.85 million birds. The migrant population has remained relatively stable since 1990, with the population in 2012 estimated at 1.84 million. Resident geese comprised 68% of the total Canada goose population in 2012 compared to 41% in 1990 and 20% in 1970. From 1990 to 2012, 1,403 Canada goose strikes with civil aircraft were reported in the USA, of which 704 (50%) caused damage. The strike rate and damaging strike rate for all geese and for resident geese only (strikes in May to September) increased in parallel with the increase in the total Canada goose population (resident and migratory combined) and resident population, respectively, from 1990 to 1999. From 1999 to 2012, the strike rate and especially the damage strike rate exhibited a downward trend, especially for strikes involving resident geese during May to September. We hypothesize that this decline is due to Canada goose management programs implemented at many airports and in other urban areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".