Evaluating the relationship between trapper harvest of American martens (<i>Martes americana</i>) and the quantity and spatial configuration of habitat in the boreal forests of Ontario, Canada
Bibliographic record
Abstract
We investigated the relationship between trapper harvest of martens (Martes americana) and the quantity and spatial configuration of marten habitat on traplines in the eastern and western boreal forests of Ontario. We used region-specific habitat models to estimate the total amount of suitable marten habitat on each trapline, and the proportion of each trapline identified as suitable habitat in various patch size classes. To control for variability in trapper success not associated with habitat, we included an index of trapper effort, as well as variables related to access, temperature, and precipitation as covariates in our regression analyses. Region-specific habitat models identified a positive relationship between the proportion of suitable marten habitat on traplines (irrespective of patch size) and trapper success. Although there did not appear to be an effect of patch size on trapper success in the eastern study region, we observed an effect in the western region. Results from the western study region suggest that traplines with suitable habitat in patches ≥500 ha will have higher trapper success than traplines with similar proportions of suitable habitat but distributed in smaller patches. Our study was conducted in a forested landscape (80% of trapline area was forested). Therefore, our findings should not be applied to areas where suitable marten habitat lies in a matrix comprised of a significant amount of non-forested area.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".