Fine-scale tertiary-road features influence wildlife use: a case study of two major North American predators
Bibliographic record
Abstract
Roads have become a major concern for wildlife managers. Determining if fine-scale features influence wildlife road use is crucial information when developing management strategies to protect species at risk or to assist in preventing negative trophic interactions. We investigated the effects of fine-scale habitat and road-related features on the tertiary-road use of two major predator groups, the American black bear ( Ursus americanus ) and wolves ( Canis lupus , C. lycaon , and hybrids). Scat occurrence, used as a measure of a species’ intensity of use, along with several road-related features and surrounding fine-scale habitat variables, were recorded within tertiary-road segments near Sudbury, Ontario, Canada. An information theoretic approach was used to determine which of several different candidate models best predicted tertiary-road use by our major predator groups. Road width and distance to primary roads were found to be the strongest predictors of occurrence on tertiary roads for both predators, with smaller road width and greater distances to primary roads leading to higher levels of occurrence. Habitat cover and cover type, expected to influence foraging opportunities, were not found to be strong predictors of tertiary-road use. Our findings highlight the importance of fine-scale studies for understanding road use.
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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".