Using ungulate biomass to estimate abundance of wolves in British Columbia
Bibliographic record
Abstract
ABSTRACT Management of wolves ( Canis lupus ) in British Columbia, as with most other Canadian provinces, is conducted on a regional scale (38,557–252,776 km 2 ), yet there is no standardized, cost‐effective methodology for providing reliable estimates of wolf abundance at this scale. Therefore, we used periodic estimates of ungulate abundance and incorporated them into an ungulate biomass regression model to estimate wolf abundance on a regional and provincial (900,402 km 2 ) scale over a 12‐year period (2000–2011). In 2011, the provincial estimate was 8,688 (95% CI = 5898–11,760) wolves (7–13 wolves/1,000 km 2 ), while regional wolf abundance estimates ranged from 149 (95% CI = 100–205) to 2,693 (95% CI = 1,818–3,608) with differences related to regional scale (km 2 ) rather than wolf densities (4–15 wolves/1,000 km 2 ). We suggest the ungulate biomass regression model is useful to estimate the abundance of wolves for management purposes when precise estimates are not required and wolf populations are not heavily exploited or recovering. © 2014 The Wildlife Society.
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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 | 0.001 | 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) | 0.001 | 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".