Assessing cumulative impacts of forest development on the abundance and distribution of furbearers.
Bibliographic record
Abstract
Furbearer populations across the central-interior of British Columbia, Canada, are exposed to the cumulative impacts of landscape change, particularly as a result of forest harvesting. I elicited knowledge from furbearer experts to develop habitat models for three furbearer species: fisher (Pekania pennanti), Canada lynx (Lynx Canadensis), and American marten (Martes americana), and applied the models to reference landscapes to quantify changes in habitat availability and quality from 1990 to 2013. Where forest harvesting was extensive, the models predicted substantial declines in habitat for each focal species. I used trapping records and negative binomial count models to investigate the relationship between habitat change and population abundance of lynx and marten. The top-ranked count models identified combinations of trapping effort, trapline area, and habitat availability and quality as having significantly positive effects on capture success. These results demonstrate the utility of expert knowledge for studying cumulative impacts of landscape change on furbearers. --Leaf ii.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".