Feasibility of wolf reintroduction to Nova Scotia: public opinions on wolves and their management in light of the ecological potential for wolf recovery
Bibliographic record
Abstract
This study investigated the ecological and social potential for wolf (Canis spp.) recovery in Nova Scotia, Canada. Reintroduction potential was considered through a GIS-based analysis of land cover, human population density, land ownership, prey density, and road density. Two disconnected areas of adequate habitat for wolves were identified. Qualitative interviews were conducted with seven identified groups on public attitudes towards the wolf and its potential recovery in the province. Opinions ranged from ‘love’ to a strong dislike of wolves, and many interviewees associated wolves with fear and expressed concern that they would come into contact with wolves on or near their properties. It would likely not be advisable to introduce an active wolf reintroduction program in NS at this time, due to the absence of effective habitat connectivity between the two identified areas of suitable habitat, and the public unease about wolf proximity. However, a proactive public education initiative is recommended in case of future reintroductions or natural immigrations of wolves and other top carnivores from nearby populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".