Resource selection by coyotes (<i>Canis latrans</i>) in a longleaf pine (<i>Pinus palustris</i>) ecosystem: effects of anthropogenic fires and landscape features
Bibliographic record
Abstract
Prescribed fire is used to restore and maintain fire-dependent forest communities. Because fire affects food and cover resources, fire-mediated resource selection has been documented for many wildlife species. The first step in understanding these interactions is to understand resource selection of the predators in a fire-maintained system. We attached GPS radio collars to 27 coyotes (Canis latrans Say, 1823) and examined resource selection relative to fire-maintained vegetation types, years since fire, anthropogenic features that facilitate prescribed burning, and other landscape features likely to affect coyote resource selection. Coyote home ranges were characterized by more open vegetation types and more recently burned forest (i.e., burned 0–1 year prior) than available on the study area. Within their home ranges, coyotes avoided areas close to densely vegetated drainages and paved roads. Coyote selection of more recently burned forest likely was in response to greater prey density or increased ability to detect prey soon after vegetation cover was reduced by fires; similarly, coyotes likely avoided drainages due to decreased hunting efficiency. Coyote resource selection was linked to prescribed fire, suggesting the interaction between fire and coyotes may influence ecosystem function in fire-dependent forests.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".