Traffic volume and highway permeability for a mammalian community in the Canadian Rocky Mountains
Bibliographic record
Abstract
We examined whether highway traffic volume changed the rates of movement (habitat permeability) for ten mammalian species in the central Canadian Rocky Mountains. Winter track count data were collected on four highways of varying traffic volume: the Trans‐Canada Highway (TCH) (14,000 annual average daily traffic [AADT]) and 1A Highway (3,000 AADT) in Banff National Park and the Highway 40 (5,000 AADT) and Smith Dorrien Trail in Kananaskis Country (2,000 AADT). Permeability represented the ratio of road crossing tracks/km to tracks/km on transects adjacent to roads. We compared permeability at the community level and for carnivore and ungulate guilds, using a Kruskal–Wallis H ‐test. Traffic volume significantly reduced habitat permeability for the community ( P < 0.05). Pair‐wise Kruskal–Wallis tests showed that habitat permeability was significantly reduced for carnivores at high traffic volume ( P = 0.008) and for ungulates at very high traffic volume ( P < 0.043). Cross‐referencing with winter traffic counts, we found movement was impaired for carnivores when traffic ranged from 300 to 500 vehicles per day (VPD) and for ungulates between 500 and 5,000 VPD. Our results indicated that the TCH requires mitigation to restore habitat permeability for all species and yielded strong evidence that the Highway 40 is a priority for mitigation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".