The Influence of Prey Availability and Vegetation Characteristics on Scent Station Visitation Rates of Coyotes, <em>Canis latrans</em>, in a Heterogeneous Environment
Bibliographic record
Abstract
We investigated the effects of local prey fluctuations and habitat variables on the scent station visitation rates of the Coyote (Canis latrans) in northern Illinois within a heterogeneous environment. Availability of small mammalian prey was assessed by monthly mark-recapture sampling and visual counts conducted along three, 192-m transects in each of seven habitats that ranged from grassland to wooded sites. Habitat metrics, which included foliage density, ground cover, and canopy cover, were also collected for the same seven habitats. Visitation rates of Coyotes were determined from scent station lines parallel to the small mammal trapping transects. A multiple regression analysis indicated that Coyote visitation rates across the study site were influenced positively by vole (Microtus spp.) abundance and negatively by canopy cover. When Coyote visitation rates were regressed on vole abundance for only the habitats in which voles occurred, the relationship was not significant. This may be attributed to the general avoidance of wooded areas by Coyotes. Coyotes did, however, respond to experimentallyinduced abundant patches of Peromyscus. These findings suggest Coyotes selectively use grassland habitats within a heterogeneous environment and may modify their use according to prey availability.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".