A review of population-based management of Southern mountain caribou in BC
Bibliographic record
Abstract
The decline in Mountain caribou in BC over the past decades has resulted in extensive basic and applied research to guide caribou management by understanding the proximate and ultimate causes of the decline. At the top of the list is high predation rates brought about by human alteration of habitat and the subsequent alteration of predator-prey dynamics. We reviewed population-based management experiments undertaken in BC to recover caribou populations. These included primary prey (moose) reduction, lethal predator control, maternal penning, translocations, and supplemental feeding. Moose reduction by liberalized harvests has led to wolf population reduction and stabilization of treated caribou herds. Translocations have had limited success and finding source populations for future attempts will be a challenge. Maternity pens are producing promising results but their efficacy is strongest in relatively small populations and best results appear to occur when combined with predator control in areas surrounding the pens. Predator control experiments have just begun and results are forthcoming. Overall, no single population-based management tool has increased caribou populations. It is recommended that multiple levers need to be applied in combination on an experimental basis going forward.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".