Safe and Selective Capture of Bobcats (<i>Lynx rufus</i>) Using Trained Hounds in the Absence of Snow
Bibliographic record
Abstract
In January 2010, we were contracted to collect biological samples from 25 wild Lynx rufus (Bobcat) for felid disease studies. We collected all samples in compliance with state and federal research guidelines on public land within the Puma concolor coryi (Florida Panther) primary range. This area extends south of the Caloosahatchee River to the tip of peninsular Florida. To maximize selectivity and minimize the risk of injury to target and non-target species, we chose hounds trained according to our specific regimen to capture felines and ignore non-target species. We conducted fieldwork from 23 February 2010 to 5 May 2010. During this period, we safely captured 25 Bobcats in 36 d of effort. After we collected biological samples, we marked each Bobcat with a yellow ear-tag to prevent redundant immobilizations. The results of this project demonstrate that specially trained hounds are a safe and selective alternative for the capture of small cats, even in the absence of snow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".