Why didn’t the caribou (Rangifer tarandus groenlandicus) cross the road? The barrier effect of traffic on industrial winter roads
Bibliographic record
Abstract
Abstract Barren-ground caribou are in steep decline due to the combined effects of climate change, natural population fluctuations, and anthropogenic disturbance. For the Indigenous peoples that rely on caribou for subsistence and cultural continuity, this decline presents a grave threat to a way of life. Wildlife managers are concerned about the potential effects of winter roads on the use of space by caribou. Roads, especially those with high levels of traffic, act as barriers to movement by ungulates. In the central Northwest Territories, Canada, the Tibbitt to Contwoyto Winter Road services diamond mines located on the winter range of several populations of barren-ground caribou. Impeded movement could restrict the distribution or influence the habitats used by caribou during winter. We investigated the influence of traffic volume and other disturbance and environmental variables on the road-crossing decisions of caribou. We used logistic regression to contrast observed and available crossing events by caribou that were recorded using high-precision GPS collars during 2018–2020. Of 62 collared caribou that moved near the winter road, only 33 crossed the winter road, for a total of 100 crossing events. Caribou rarely crossed the road when any level of traffic was present; the level of traffic, not the road right-of-way, was the underlying explanatory factor for that behavioural decision. Our results suggest that mitigation and associated monitoring should focus on strategies that minimize traffic volume or provide breaks in traffic when caribou are adjacent to winter roads.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".