Estimating unrecorded human-caused mortalities of grizzly bears in the Flathead Valley, British Columbia, Canada
Bibliographic record
Abstract
Managing the number of grizzly bear ( Ursus arctos ) mortalities to a sustainable level is fundamental to bear conservation. All known grizzly bear deaths are recorded by management agencies but the number of human-caused grizzly bear deaths that are not recorded is generally unknown, causing considerable uncertainty in the total number of mortalities. Here, we compare the number of bears killed legally by hunters to the number killed by people for all other reasons, for bears wearing functioning radiocollars and for uncollared bears recorded in the British Columbia (BC) government mortality database for the Flathead Valley in southeast BC. Between 1980 and 2016, permitted hunters killed 10 collared bears and 12 (9 known, 3 suspected) were killed by people for other reasons. This ratio differed ( p < 0.0001) from the uncollared bears in the government database where 71 were killed by hunters while only 10 were killed for other reasons. We estimate that 88% (95% CI; 67–96%) of the human-caused mortalities that were not by permitted hunters were unreported. The study area may have low reporting rates because it is >40 km on a gravel road from a Conservation Officer office, so reporting is difficult and there are no human residences so there is little concern of a neighbor contacting an officer. Our results are likely indicative of other places that are road-accessed but far from settlements. We discuss the implications of sampling individuals for collaring and the possible implications of wearing a collar on the animal’s fate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".