Movement of small mammals across divided highways with vegetated medians
Bibliographic record
Abstract
Previous studies suggest the gap in forest cover generated by roads contributes to the barrier effect of roads on movement of forest-dwelling small mammals. However, it is not known if vegetated medians of divided highways affect movement of small mammals by reducing the effective highway width. The purpose of our study was to determine whether the type of vegetation cover in the median (treed or grassy) or median width affects small-mammal crossings of divided highways. At 11 study sites varying in median cover type and width, we live-trapped small mammals next to one side of the highway and translocated them to the opposite side of the highway using a standardized translocation distance. In total, 24% of translocated individuals were recaptured on the side of the highway of initial capture, i.e., they had moved across the entire highway. This was significantly lower than what would have been expected in the absence of the highway (58%). The overall probability of recapturing a translocated individual was not significantly related to median cover type or width. Our results suggest that efforts to mitigate the barrier effect of highways on small mammals cannot be accomplished by altering median vegetation type and width.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".