Assessment of corridors for movement of Gray wolf (Canis lupus) across rural land between two protected parks in southwestern Manitoba, Canada
Bibliographic record
Abstract
Riding Mountain National Park (RMNP) occupies 2,974 km2 of mixed wood boreal forest in southwestem Manitoba that is almost completely surrounded by agriculture. There is concern that wide-ranging, large carnivore populations in the park are genetically isolated and consequently nonviable over the long term. This study was carried out to identify areas with potential to support wolf dispersal from RMNP to the nearby Duck Mountain Provincial Park and Forest Reserve (DMPP&F) across the human disturbed land outside the park boundaries. Wolf telemetry data from RMNP provided information about preferred habitats within a protected and relatively undisturbed area. Presence of wolves between the parks was gathered from personal interviews with local landowners as well as wolf tracks. It was found that wolves avoid human disturbed areas within RMNP and select undisturbed areas outside the park boundaries. Furthermore, negative attitudes towards wolves held by local residents and its associated mortality threat comprise the major barrier to wolf-movement between the parks. A regionally connected wolf population depends on protection of remaining undeveloped land between the parks and acceptance by resident humans. Long term viability of the regional wolf population further relies on protection of wolves in the whole area and joint management amongst stakeholders at all levels.
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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 | 0.002 | 0.000 |
| 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".