The Effects of Native Vegetation Buffer on the Canadian Geese Population in Vilas Park
Bibliographic record
Abstract
The Canadian geese population throughout most parts of North America has increased over the years and has become a nuisance in some suburban and urban areas. There are particular problems, especially in the area of Vilas Park where there are soccer fields and playgrounds (known as zone 2 – Appendix A). As a way to minimize the number of geese and goslings that are in zone 2, the Friends of Lake Wingra and the City of Madison planted a native vegetation buffer along the shoreline of Lake Wingra in the zone 2 area. This planting took place in the early summer of 2005 and continued in 2006. In an effort to measure the effect of the native vegetation buffer on the number of geese and goslings in zone 2, we analyzed data collected by students at Edgewood College. The data include geese and gosling counts taken over the past several years throughout Vilas Park. The data indicates that the native vegetation buffer has been effective in reducing the number of geese and goslings in the zone 2 area during months when the vegetation is present. It seems reasonable that the Friends of Lake Wingra and the City of Madison should expand their effort to plant the native vegetation into other zones where geese are deemed to be a nuisance.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".