Grizzly bear use of open, closed, and restricted forestry roads
Bibliographic record
Abstract
We investigated grizzly bear (Ursus arctos) selection of three road types in the northern United States and southern British Columbia from 1986 to 1991. We hypothesized that grizzly bears select against open (public use allowed), restricted (forestry use only), and closed roads (no public use allowed) in that order. We analyzed use of roads for 11 bears (five females and six males) in an area containing open and closed roads and 11 bears (seven females and four males) in an adjacent area containing restricted roads. We used χ 2 and loglinear models to test for selection of habitat type and distance to road categories. Ten of 12 females and 5 of 10 males (15 of 22 bears) selected against (P < 0.05) low-elevation interior cedar-hemlock and for (P < 0.05) high-elevation Englemann spruce (Picea engelmannii Parry ex Engelm.) subalpine fir (Abies lasiocarpa (Hook.) Nutt.). After accounting for habitat, 4 of 5 females and 3 of 6 males (7 of 11 bears) selected against open roads and 3 of 5 females and 0 of 6 males (3 of 11 bears) selected against closed roads. No females (n = 7) or males (n = 4) (0 of 11 bears) selected against restricted roads. Our results are inconsistent with the hypothesis that bears select against open, restricted, and closed roads in that order. Most females and males selected against open roads, most females selected against closed roads, and no bears selected against restricted roads. The type of human activity along roads plays a role in bear responses to roads, and this aspect should be incorporated into future bear-road studies.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".