Factors affecting the permeability of road mitigation measures to the movement of small mammals
Bibliographic record
Abstract
Mitigation measures, such as wildlife-exclusion fencing and crossing structures (overpasses, underpasses, culverts), have been widely demonstrated to reduce the negative effects of roads on medium-sized and large animals. It is unclear how these mitigation measures influence the movement of small mammals (<5 kg). Our study has three objectives: (1) to test whether culverts improve highway permeability; (2) to determine factors associated with culvert use, such as culvert obstruction by snow; (3) to evaluate factors contributing towards fence permeability, such as the presence of a culvert, snow depth, and fence mesh size. We used snow tracking to assess the movement for four small-mammal taxa along the Trans-Canada Highway corridor in Banff National Park, Alberta, Canada. We found that the presence of a culvert within 100 m of transects significantly improved fence and highway permeability. Obstruction of the culvert entrance by snow was negatively correlated with the probability of use, and therefore, of highway permeability. Furthermore, the mesh size of the fencing did not affect fence or highway permeability. We recommend that culvert entrances be located on the outside of fenced right-of-ways to reduce obstruction by highway maintenance activities such as snowplowing.
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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 | 0.004 |
| 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) | 0.002 | 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".