Bibliographic record
Abstract
ABSTRACT Trapping for furbearers remains an important outdoor activity in Alberta, Canada, despite low fur prices and extensive industrial development. We investigated the influence of landscape change on furbearer harvests using 30 years of marten ( Martes Americana ) harvest records, interviews with trappers, and Geographic Information System maps of industrial activity and vegetation types. We used an information‐theoretic approach to explore variation in trapper success. Cover type and landscape metrics apparently influenced trapper success, because traplines where martens were consistently caught had less vehicle and all‐terrain vehicle access, fewer oil and gas wells, and greater proportion of mature conifer forests than traplines where martens were infrequently caught. We identified an important cutoff value or statistical threshold that identified 45% closed‐conifer cover, suggesting that a minimum amount of forest cover is crucial for trappers to catch martens. We conclude that the nature and extent of industrial disturbance is contributing to the decision by trappers to trap as well as influencing their success. We recommend that wildlife managers collect trapping effort information (i.e., species‐specific no. of trap‐nights) on fur reports in association with landscape changes to monitor furbearer harvests more effectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".