Habitat security and diets for recovery of Alberta grizzlies: lessons from coastal BC, Alaska and Yellowstone
Bibliographic record
Abstract
Brown bears (Ursus arctos) in North America vary widely in their densities from a maximum of 550 bears /1000 km2 in coastal Alaska to less than 5 bears /1000 km2 for mountain bears in the north; this variation has been attributed to differences in food base. The impacts of security and perceived risk on the exploitation of energy rich environments also have significant impacts on demographic rates within populations. Increasing the energy density of habitat has been identified as an important step in the restoration and maintenance of small brown bear populations in Europe and this is equally applicable to bear populations at risk in North America. Where bears persist at high densities they are in productive ecosystems, where protection has been of low productivity land populations which survive are marginal. Drawing on examples from Yellowstone, coastal British Columbia and Alaska we will present the case for using areas of enhanced habit security and energy density as source populations within a source-sink model of conservation of a species at the edge of its current range to halt the retreat of bears in Alberta.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".