Do trappers understand marten habitat?
Bibliographic record
Abstract
Abstract Previous studies of the effects of fur trapping on marten populations have not considered habitat variation and how trappers use available habitat. We investigated the behavior of fur trappers with respect to roads, waterways, and the forest habitats on trap lines, using registered trap lines in northern Ontario as a study system. The objectives of this study were to 1) develop models for predicting trap location based on access and habitat features, 2) determine whether trappers target the same habitat preferred by American marten, and 3) investigate effects of spatial resolution on predictive models, using a geographic information system (GIS) for coarse resolution variables and direct forest mensuration for fine resolution variables. Distance to roads and water were by far the most influential factors in logistic models for predicting trap presence, accounting for 51.2–61.7% of the observed deviance. At a coarse spatial resolution, trappers selected sites that were close to vehicular access, and in older mixed wood forest stands. Similarly, at a coarse resolution, marten selected old stands, but dominated by coniferous trees. At a finer spatial resolution, trappers selected sites with high basal area of trees, pronounced proportion of black spruce, high canopy cover, and high density of coarse woody debris, consistent with previous studies on marten habitat selection at a fine resolution. Although coarse resolution models are easily applicable because of the wide availability of GIS land cover data, fine resolution models had greater predictive power when considering habitat variables. By quantifying trapper behaviors, these results suggest that the effectiveness of marten sanctuaries used in forest management depend not only on the age and species composition of forest stands left unlogged, but also on the degree to which they are accessible to trappers. © 2012 The Wildlife Society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".