Spatial and Temporal Patterns of Wolf Harvest on Registered Traplines in Alberta, Canada
Bibliographic record
Abstract
Abstract: Gray wolf ( Canis lupus ) populations are recovering in many parts of the world and managers from various jurisdictions will be faced with difficult decisions as wolf populations continue to increase. Wolf management in Alberta, Canada, is achieved mostly through trapping on a registered trapline system. Wolf harvest increased over the last 22 years relative to population increases. Most wolf harvests occurred in the Rocky Mountains and surrounding foothills area and this pattern was consistent over time. On average, trappers only harvested an estimated 9.8% of the provincial population annually despite the lack of bag limits or quotas. Harvests are spatially autocorrelated with peak autocorrelation coinciding with average home‐range size for wolves in Alberta. When wolf control actions are deemed necessary, trappers are unlikely to remove a sufficient number of wolves over a large enough area to limit subpopulations under the registered trapline system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".