Assessing Movement, Site Preferences, and Environmental and Social Impacts of Canada Geese across an Urban Landscape
Bibliographic record
Abstract
Resident Canada goose (Branta canadensis maxima) populations have increased, causing many human-goose conflicts. These include decreased water quality, aggressive behavior towards humans and pets, the risk of disease from fecal contamination, and the potential for bird strikes to aircraft. To better understand these human-goose conflicts and potential risk to airport safety, we will examine Canada geese movements, habitat use, human attitudes toward geese, and disease transmission on the Piedmont-Triad International airport and surrounding areas of Greensboro, North Carolina. We will use a variety of tools, such as color-marking with auxiliary neck bands, satellite telemetry with global positioning system harnesses, blood/fecal sampling for disease, and surveys to evaluate the effect of Canada geese across an urban environment. After a year of observation, we will remove geese from areas where removal is most likely to reduce goose-aircraft collisions. Following the removals, we will monitor goose movements around the airport and the rates of re-colonization of removal sites. Using the information obtained from the study, we will develop management recommendations for reducing human-goose conflicts (e.g., removals to reduce risk of goose-aircraft collisions, disease transmission potential).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".