Evaluation of resident Canada goose movements to reduce the risk of goose‐aircraft collisions at suburban airports
Bibliographic record
Abstract
ABSTRACT Resident (non‐migratory) Canada goose (Branta canadensis) populations in suburban environments pose risks to human health and safety. Specifically, the relatively large size and gregarious behavior of geese combined with an overlap in aircraft flight space pose substantial risk of property damage and human fatalities from goose‐aircraft collisions. We estimated home range and core use areas of resident Canada geese and evaluated goose movements to better define the risk of goose‐aircraft collisions around Piedmont Triad International Airport in Greensboro, North Carolina, USA. We placed satellite transmitters on 16 of 763 neck‐ and leg‐banded geese to identify and track individuals over an 18‐month study period. The frequency of satellite‐tagged goose movements peaked daily within the first 2 hours after sunrise (28.1%) and again near sunset (27.2%). All in‐flight goose movements occurred ≤64 m above ground level. Geese flying at these altitudes posed a risk to aircraft in the take‐off and landing phases of flight. For all in‐flight movements, the number of movements per day was 0.13 during the molt (1 Jun–15 Jul), 0.42 during early post‐molt 2008 (16 Jul–31 Oct), 0.36 during late post‐molt (1 Nov–31 Jan), 0.58 during breeding/nesting (1 Feb–31 May), and 0.58 during the early post‐molt 2009. Satellite‐tagged geese traveled a mean distance ranging from 2.0 km (SE = 0.3) to 4.9 km (SE = 0.4) per day, depending on sex and season, which supports the need for intensive goose management within a minimum distance of 8 km from airports. Mean fixed 95% kernel home range and 50% core use area were 991.8 ha (SE = 241.1) and 120.4 ha (SE = 24.6), respectively. Additionally, we monitored site recolonization of nuisance geese after the controlled removal of 60 resident geese from 1 site, which eliminated 24.2% of those initially banded at the site in 2008, but other geese began to recolonize the site within 27 days. Rapid recolonization of the removal site suggests that lethal removal should be conducted at all molt locations within a minimum distance of 8 km of suburban airports and any additional removal or management resources should be applied to greater distances to prevent recolonization of these sites by geese in close proximity to the removal site. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.000 |
| 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".