Effectiveness of Citizen Engagement in a State Agency Egg-Oiling Program to Reduce Urban Canada Goose Populations and Conflicts
Bibliographic record
Abstract
In many cases, urban environments create high-quality habitat for wildlife that may allow species to form commensal relationships with humans. Positive impacts of urban environments (e.g., high-quality nesting habitat) may enhance survival and/or increased fecundity. One species that has shown the ability to form commensal relationships with humans in urban environments is Canada geese (Branta canadensis). Canada goose populations have continued to rebound in Kansas, USA, and across the Great Plains, such that the estimated resident population in Wichita, Kansas, grew from 0 to >5,000 resident geese and the over-winter population increased from 1,600 in 1997 to >18,000 by 2003. Due to the increase in the urban Canada goose population, the number of complaints concerning geese has steadily increased through time. In response to complaints, the Kansas Department of Wildlife and Parks implemented an egg-oiling program in 2002, to assist in reducing the resident goose population. Previous studies have suggested effective goose population control programs must include reducing adult survival through efforts such as hunting and that egg-oiling programs alone may not be successful in reducing urban Canada geese populations. We examined the effects of a citizen-involved egg-oiling program from 2002 to 2020 to reduce the resident Canada goose population in Wichita, Kansas. We assumed every female Canada goose survived to age 20 and using survival estimates from the literature for different life stages, we developed program impact models to demonstrate effectiveness of the Kansas Special Canada Goose Permit program. We estimated a mean 128,676 (range: 89,749–167,663) potential adult, resident geese being removed from the population in the Wichita, Kansas, area from 2003 projected out to 2040. Our results suggested that egg-oiling programs conducted at large spatial and temporal scales and at low cost by citizens may limit population growth of urban geese.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".