Addendum to “Managing wolves (<i>Canis lupus</i>) to recover threatened woodland caribou (<i>Rangifer tarandus caribou</i>) in Alberta”
Bibliographic record
Abstract
Managing predators to restore threatened or endangered species is often controversial. Hervieux et al. (2014; Can. J. Zool. 92(12): 1029–1037) report on the efficacy of wolf (Canis lupus L., 1758) reduction as a recovery strategy in the Little Smoky population of boreal woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) range in Alberta, which generated a lot of media attention. As such, the authors were invited by the journal editor who handled the original paper to write this addendum to provide clarification regarding the methodology used in the original paper. Wolf reduction was conducted by Government personnel in accordance with appropriate policy and laws (i.e., federal and provincial Species at Risk Acts; Alberta Wildlife Act; Alberta Woodland Caribou Policy). University-based researchers were involved only in data analysis and writing, and thus did not require approval by a university-based animal welfare board. Collaboration between independent university-based scientists and government biologists is essential to effective assessment of such controversial management practices. Hervieux et al. (2014; Can. J. Zool. 92(12): 1029–1037) in fact concluded that such wolf reductions, by themselves, would only “buy time” and would not restore woodland caribou alone without effective habitat protection.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.177 | 0.058 |
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".