Characterizing Moose–Vehicle Collision Hotspots in Northern British Columbia
Bibliographic record
Abstract
Abstract To have a better understanding of the ecological factors that may contribute to moose Alces alces and vehicle collisions in northern British Columbia, we analyzed Wildlife Accident Reporting System data that were collected between 2000 and 2005 by highway maintenance contractors. We delineated 29 moose-vehicle collision hotspots and 15 control sites at which we assessed environmental and road infrastructure attributes through field surveys and remotely sensed data. A logistic regression model including both coarse- and fine-scale environmental factors suggested that hotspots were more likely to be characterized by the number of roadside mineral licks and bisection of the highway corridor through black spruce forest–sphagnum bog habitat and swamps. The absence of rivers within 1 km and less lake area within 500 m of the highway also better characterized hotspots than controls. At the fine scale, deciduous forest cover along the highway edge and the proportion of browse to nonbrowse vegetation between the road shoulder and forest edge were also related to collision sites. Based on these data, the mitigation of collision hotspots should include decommissioning roadside mineral licks where they occur and cutting roadside brush to improve driver visibility and reduce browse resprouting and attractiveness. Where new road construction or road realignments are being contemplated, we recommend considering routes with more lake area, more rivers, fewer swamps, and fewer black spruce forest–sphagnum bog habitats to help reduce collisions. We discuss the utility of installing novel warning signage in areas where collisions are recurrent.
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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".