No statistical support for wolf control and maternal penning as conservation measures for endangered mountain caribou
Bibliographic record
Abstract
Abstract Mountain caribou, a behaviourally and genetically distinct set of ecotypes of the Woodland caribou ( Rangifer tarandus caribou ) restricted to the mountains of western Canada, have undergone severe population declines in recent decades. Although a broad consensus exists that the ultimate driver of these declines has been the reduction of habitat upon which mountain caribou depend, research and policy attention has increasingly focused on predation. Recently, Serrouya et al. (Proc Nat Acad Sci USA 116:6181–6186, 2019) analysed population dynamics data from 18 subpopulations in British Columbia and Alberta, Canada, subject to different treatments and ‘controls’, and concluded that lethal wolf control and maternal caribou penning provide the most effective ways to stabilize population declines. Here we show that this inference was based on an unbalanced analytical approach that omitted a null scenario, excluded potentially confounding variables and employed irreproducible habitat alteration metrics. Our reanalysis of available data shows that ecotype identity is a better predictor of population trends than any adaptive management treatments considered by Serrouya et al. Disparate behavioural characteristics and responses to industrial disturbance among ecotypes suggest it may be incorrect to assume that adaptive management strategies that might benefit one ecotype are transferable to another.
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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 | 0.039 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".