Conservation of grizzly bears using access management
Bibliographic record
Abstract
Grizzly bears avoid roads in some areas but select areas near roads in others. This is driven by mechanisms such as traffic patterns and food resources near roads. Understanding what mechanisms drive the relationship between grizzly bears and roads is of particular importance in Alberta where the majority of grizzly bear mortalities occur within 500 m of a road. We modelled a suite of potential mechanisms underlying grizzly bear selection or avoidance of roads and tested which of these best predicted grizzly bear habitat use and movement around roads. A combination of food, traffic, and large-scale landscape variables best predicted grizzly bear distribution. We used these results to simulate the impacts of road access changes as a result of road construction, reclamation, or gating. Our findings highlight the importance of examining the mechanisms driving habitat use and movement of large mammals in human altered landscapes. Access management, the closing of roads during certain times of the year, soon will be implemented in Alberta to conserve grizzly bear populations. Understanding the mechanisms behind grizzly bear use of roaded areas will be essential in choosing which roads to close and when to close them.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".