Capturing problematic urban Canada geese in Reno, Nevada: Goose roundups vs. use of alpha-chloralose
Bibliographic record
Abstract
Urban Canada geese, in large numbers, have exploited human manipulation of the Reno, Nevada cityscape, creating human health and safety concerns along with monetary losses to businesses and private citizens. A primary concern from the start has been the potential for an aircraft-bird strike at the Reno-Tahoe International Airport. To address this urban goose problem, USDA APHIS Wildlife Services (WS) “rounds up” the problem geese by use of a funnel trap, where a gathering cage is placed at the junction of two plastic fence wings. Goose roundups occur on golf courses, city parks, and private residences. The use of the traps can be labor intensive on some properties, often requiring the coordination of federal, state, and city employees in addition to volunteers, to assure adequate personnel are available. A goose roundup is generally a high-profile public affair. WS makes every effort to start roundups early in the morning (4:00-4:30 A.M.) to avoid crowds, and more importantly, to reduce heat stress to the geese. However, there always seem to be a few citizens observing the operation. For many people, the sight of 20 to 30 geese enclosed in a small cage, honking and hissing, and potentially trampling goslings under foot, can be quite upsetting. Many urbanites only encounter Canada geese at the city park, where they typically enjoy feeding the local population. This limited exposure often results in negative reactions to goose capture and relocation efforts. To reduce the need for high-profile roundups in urban areas, WS experimented with the use of alpha-chloralose (AC) on urban geese located in select parks and gated communities. Alpha-chloralose is an avian tranquilizer that is administered orally to waterfowl through corn or bread bait. We hoped that the use of AC would change public opinion about the stress the geese might suffer during capture efforts. Further, we sought to capture repeat offending geese that had become wise to the funnel trap and avoided capture by that method. Since many properties are not suitable for funnel trap use, we wished to expand the WS urban goose management effort by employing this additional capture method. AC delivery by bread baits allowed for the precise targeting of problem geese. The advantages and disadvantages of both roundups and AC treatments are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".