Fencing Alleviates Nuisance Molting Goose Problems in an Urban Park in Tennessee
Bibliographic record
Abstract
Canada geese (Branta canadensis; geese) often congregate in high public use areas while molting during summer, resulting in increased nuisance complaints. We censused geese that inhabited a Cookeville city park in Tennessee, USA on a weekly basis from 2013-2014 to determine the magnitude, trends, and seasonal nature of problems caused by urban goose flocks. Fewer than 50 geese were counted in most months except during the molt, when numbers increased to over 200. Most geese dispersed from the park shortly after completion of the molt. Molt site fidelity to the park was estimated to be 51.5%, indicating that permanent relocation or euthanasia would not provide long-term nuisance relief and may impact local hunting opportunities. To mitigate the nuisance aspect of high densities of molting urban geese in the park, we herded molting and flightless geese to a closed portion of the park and fenced them out of the public use area. Our temporary fencing, coupled with reduced human disturbance in the area where geese were relocated, alleviated the nuisance problems typically associated with large concentrations of geese. We recommend that other municipalities that are experiencing similar seasonal nuisance goose problems consider using non-lethal fencing options.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".