Vegetated highway medians as foraging habitat for small mammals
Bibliographic record
Abstract
ABSTRACT Wildlife passages are intended to facilitate movement of animals across roads while mitigating barrier effects. A less appreciated aspect of mitigation is how such wildlife passages may be used for reasons that are supplementary to their intended purpose of facilitating movement over or under roads, particularly when a vegetated median is present and safely accessible. We documented 97 instances of mammals <5 kg foraging in vegetated highway medians within the highway corridor during wildlife passage crossings. From 2012 to 2015, we monitored 6 wildlife passages using infrared cameras in Quebec, Canada, along a divided, 4‐lane highway with a vegetated median. Weasels ( Mustela spp.) were the most likely species to forage in the vegetated median after accessing it using a wildlife passage, followed by North American red squirrels ( Tamiasciurus hudsonicus ), eastern chipmunks ( Tamias striatus ), micromammals (shrews, mice, voles, and moles), and American mink ( Neovison vison ). Foraging duration did not differ among species groups. Our observations demonstrate that wildlife passages that include natural habitat in their design, such as by maintaining safe access to a vegetated median within the highway corridor, provide foraging opportunities for some mammal species. We encourage further studies evaluating how variations in structure design may affect movement across or along roads. © 2019 The Wildlife Society.
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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 | 0.000 |
| 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 | 0.000 | 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".