Grizzly bear selection of managed and unmanaged forests in the Selkirk Mountains
Bibliographic record
Abstract
We tested the commonly held hypotheses that grizzly bears (Ursus arctos) select against clearcuts and young forests and select for natural openings and old forests in the Selkirk Mountains from 1986 to 1991. We compared use versus availability using χ2 goodness-of-fit for 11 bears (five females, six males) in a south study area containing both open roads (public use allowed) and closed roads (no public use allowed) and 11 bears (seven females, four males) in a north study area containing restricted roads (forestry use only). Zero of 11 females and 1 of 11 males (1/22 bears) selected against (P < 0.05) clearcuts. Five of 11 females and 2 of 11 males (7/22 bears) selected against (P < 0.05) young forests. The apparent selection against young forests appeared to be due to selection against associated open roads, not against young forests themselves. Forestry activities alone (managed forests and restricted roads) appeared to have no negative impact on grizzly bear habitat use. Because of small sample sizes, pooling of seasonal data, and lack of experimental replication, our results should not be extrapolated until similar studies are conducted elsewhere.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".