Presence of Mammals in Ontario, Canada, Verified by Trail Camera Photographs Between 2008 and 2010
Bibliographic record
Abstract
Trail cameras were used to determine the presence of medium to large-sized wild mammals in Ontario between 2008 and 2010. A total of 27 different species of mammals across the province were photographed during 17308 trail-camera-nights. Presence indices (photographs per trail-camera-night) for the areas sampled in southern Ontario were highest for the following species: White-tailed Deer (Odocoileus virginianus), Raccoon (Procyon lotor), Coyote (Canis latrans), Eastern Gray Squirrel (Sciurus carolinensis), and Red Squirrel (Tamiasciurus hudsonicus). Presence indices for the areas sampled in northern Ontario were highest for White-tailed Deer, American Black Bear (Ursus americanus), Moose (Alces alces), Snowshoe Hare (Lepus americanus), and Red Squirrel. Trail camera photographs depicted extensive use of snowmobile trails by wildlife in southern Ontario.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".