Quantifying Coyote (Canis latrans) Abundance and Habitat Use in the Long Point National Wildlife Area Using Camera Traps
Bibliographic record
Abstract
Coyotes (Canis latrans) are a widespread predator within North America, acting as a keystone species in a variety of ecosystems. Within protected areas, understanding the role of coyotes as a top predator is important for effective management. However, studying coyotes is challenging as the species is elusive and highly plastic in their abundance, spatial temporal distribution, habitat selection, and choice of prey. Within the Long Point National Wildlife Area (LPNWA) coyotes have been identified as a top predator but management lacks up-to-date information about the coyote population. In the summer of 2022, we initiated a two-year camera trapping study using 30 cameras rotated across 90 sites in a 500 m2 hexagonal grid arranged across the LPNWA Long Point Unit. Using the observations at each site, the Random Encounter Model (REM) was applied to estimate the density of adults and pups across the breeding and pup-rearing seasons from 2022 to 2024. Generalized linear models (GLMs) were created to inform how different characteristics of the landscape (e.g., habitat type, distance to shore, topography) influence the activity of coyotes across climactic seasons. The results of this study will inform on the density and habitat use of the coyotes to provide baseline information for Canadian Wildlife Service management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".