Demographics and population trends of grizzly bears in the Cabinet–Yaak and Selkirk Ecosystems of British Columbia, Idaho, Montana, and Washington
Bibliographic record
Abstract
Abstract We summarize and report survival and cause-specific mortality of grizzly bears in the Cabinet–Yaak and Selkirk Mountains recovery zones from 1983–2002 to examine effects on the populations. Fifty-four percent of total known mortality in the Cabinet–Yaak was human-caused (n = 28) and 80% of total known mortality in the Selkirk Mountains was human-caused (n = 40). We investigated demographic values of 53 and 61 radiocollared grizzly bears (Ursus arctos) and attendant offspring in the Cabinet–Yaak and Selkirk Mountains recovery zones, respectively from 1983–2002. Nineteen mortalities of radiocollared animals or offspring were detected in the Cabinet–Yaak sample and 20 in the Selkirk Mountains. Estimated survival rates were 0.929 (95% CI = 0.091) for adult females, 0.847 (95% CI = 0.153) for adult males, 0.771 (95% CI = 0.208) for subadult females, 0.750 (95% CI = 0.520) for subadult males, 0.875 (95% CI = 0.231) for yearlings, and 0.679 (95% CI = 0.179) for cubs in the Cabinet–Yaak. Estimated survival rates for the Selkirk Mountains were 0.936 (95% CI = 0.064) for adult females, 0.908 (95% CI = 0.102) for adult males, 0.900 (95% CI = 0.197) for subadult females, 0.765 (95% CI = 0.176) for subadult males, 0.784 (95% CI = 0.178) for yearlings, and 0.875 (95% CI = 0.125) for cubs. Reproductive rates were 0.291 and 0.284 female cubs/year/adult female for the Cabinet–Yaak and Selkirk Mountains recovery zones, respectfully. The annual exponential rate of increase (r) was −0.037 for the Cabinet–Yaak recovery zone and 0.018 for the Selkirk Mountains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".