Green bridges in a re‐colonizing landscape: Wolves ( <i>Canis lupus</i> ) in Brandenburg, Germany
Bibliographic record
Abstract
Abstract Gray wolves ( Canis lupus ) are recolonizing many parts of central Europe and are a key part of international conservation directives. However, roads may hinder the reestablishment of gray wolves throughout their historic range by reducing landscape connectivity and increasing mortality from wildlife‐vehicle collisions. The impact of roads on wolves might be mitigated by the construction of green bridges (i.e., large vegetated overpasses, designed to accommodate the movement of wildlife over transportation corridors). In this study, we investigated the seasonal and diurnal use of a green bridge by wolves and three of their main prey species: red deer ( Cervus elaphus ), roe deer ( Capreolus capreolus ), and wild boar ( Sus scrofa ). We found that all four species used the green bridge. Wolves were most active in winter, whereas prey species were most active in spring and summer. All species were more active at dusk and during the night than at dawn and during the day. We found no evidence that wolf presence influenced bridge‐use by prey species, consistent with other tests of the prey‐trap hypothesis. Our results suggest that green bridges are used by wolves and prey species alike, and may foster connectivity and recolonization for these species in rewilding landscapes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".