Rancher Perceptions of the Coyote in Florida
Bibliographic record
Abstract
Throughout the continental United States and large portions of Canada and Central America, changes people make to the landscape such as the clearing of forested land and the extermination of larger predators like gray and red wolves have made the environment perfect for the adaptive coyote. Coyotes have rapidly taken advantage of these environmental shifts and expanded into new areas, now including all 67 counties in Florida and even Key Largo. Each year more people in Florida catch a glimpse of a coyote crossing a road or running across open fields, or notice coyote scat along a hiking trail–and farmers and ranchers are seeing signs of coyotes on their farms. As coyotes become a fixture of the Florida landscape, potential grows for conflict with humans. Coyotes are in Florida to stay, and understanding the agricultural community’s perception of their influence on livestock and wildlife is important to developing effective policies for coyote management. This revised 4-page fact sheet provides results of ongoing statewide surveys of ranchers in Florida regarding the influence of coyotes on their operations. Written by Raoul K. Boughton, Bethany Wight, and Martin B. Main, and published by the Wildlife Ecology and Conservation Department, January 2016. WEC 146/UW143: Rancher Perceptions of the Coyote in Florida (ufl.edu)
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".