DNA‐based population estimate for grizzly bears <i>Ursus arctos</i> in northeastern British Columbia, Canada
Bibliographic record
Abstract
Current harvest management of grizzly bears Ursus arctos in British Columbia (B.C.), Canada, is based primarily on modeling of habitat capability/suitability. No research has been conducted in the northern half of B.C. to verify these habitat‐based estimates. We estimated grizzly bear population size in a 8,527 km 2 study area in northeastern B.C. that included the east slopes of the northern Rocky Mountains (Northern Boreal Mountains ecoprovince) and the boreal plains (Taiga Plains ecoprovince) using hair removal to sample bears, microsatellite profiling to identify individuals, and mark‐recapture models. We placed bait sites encircled by barbed wire in a grid of 103 9 × 9 km (81 km 2 ) cells. In each cell a different bait site was set for 12 days in each of five sessions. We collected 2,062 hair samples from 332 sites and detected grizzly bears at 113 sites. DNA profiling of grizzly bear samples identified 98 different bears; 44 of these individuals were females, 47 were males, and the remaining seven individuals could not be sexed. Forty‐one grizzly bears were caught at >1 site. We used a closed mark‐recapture model to obtain a naive population estimate of 148 grizzly bears (95% confidence interval (Cl): 124–182). We reduced this estimate by 6.8% to account for closure bias, which resulted in an adjusted population estimate of 138 grizzly bears (95% Cl: 114–172) within the study area (16 bears/1,000 km 2 ; 95% Cl: 13–20). Within the two biophysical ecoprovinces we estimated a density (corrected for closure) of 29 bears/1,000 km 2 (95% Cl: 23–37) for the Northern Boreal Mountains and 10 bears/1,000 km 2 (95% Cl: 7–18) for the Taiga Plains. The current habitat‐based capability ratings for grizzly bears in the boreal ecoprovinces of B.C. are supported by our results in the Taiga Plains, but are lower than densities we obtained in the Northern Boreal Mountains by about half. With further testing, habitat‐based estimates of grizzly bear density in B.C. could be adjusted using the results of DNA‐based population estimates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".