Grizzly bears detected at ecotourism sites are less likely than predicted by chance to encounter conflict
Bibliographic record
Abstract
Whether ecotourism can lead to human–wildlife conflict is not well understood. In Nuxalk Territory, grizzly bear ( Ursus arctos horribilis Ord, 1815) conflict occurred ∼41–58 km downstream from ecotourism. We screened for genetic matches between individuals that encountered conflict ( n = 30) and 118 individuals detected upstream via hair snags (including 34 at ecotour sites). Of these 34, one encountered conflict. In analysis Scenario 1, we considered all detected and undetected bears in the region as freely mixing, and used Bayes’ theorem to account for imperfect detection of ecotour bears among conflict samples, deriving an estimate of 1.47 (rounded to 2). Accounting for this uncertainty, we used a probability approach to ask how large the unknown non-ecotour bear population would have to be to observe this frequency of conflict among ecotour bears (2/34) by chance. The resulting population level exceeded available estimates, suggesting ecotour bears are less likely to encounter conflict. In Scenario 2, we assumed that downstream bears are not necessarily from the same population as those sampled upstream; we compared the proportions of known ecotour and non-ecotour bears among conflict samples and found no evidence of a significant difference. Collectively, these analyses suggest other human-caused drivers of conflict.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".