Bibliographic record
Abstract
Alberta has employed lethal techniques to reduce predator populations in several wildlife-management situations, particularly in attempts to achieve goals and objectives for threatened or endangered species. Annual wolf populations reductions within and adjacent to woodland caribou population ranges in West Central Alberta are a notable example of this management approach. Almost all woodland caribou populations in Alberta are exhibiting ongoing population declines, with some populations declining at rapid rates. Current knowledge indicates that these declines are from apparent competition due to anthropogenic habitat changes, with resulting unsustainably high levels of wolf predation on woodland caribou populations. Delivery of annual wolf population reductions for two woodland caribou populations has resulted in stable or slightly increasing caribou population growth; in the absence of the wolf program at least one of the caribou populations would now be extirpated. The delivery of lethal wolf management for woodland caribou conservation and recovery in Alberta is enabled by a variety of provincial government approved management plans and policies. It is fully recognized that predator management for woodland caribou recovery must be predicated on management approaches and actions to improve caribou habitat conservation and recovery and thereby address the ultimate factors influencing apparent competition and unsustainably high levels of predation. Considerable effort is now being devoted to planning, policy revisions, regulatory adjustments, and management actions to address fundamental considerations related to caribou habitat. Progress on woodland caribou habitat will have little relevance; however, if the resident caribou population becomes extirpated before sufficient habitat recovery is achieved. Effective reductions in predation rates are needed immediately.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".