Fine-scale habitat selection of American marten at the southern fringe of the boreal forest
Bibliographic record
Abstract
American marten (Martes americana) are typically associated with mature coniferous forests. Some recent results, however, suggest that marten habitat selection may also operate at a finer scale. We therefore described site characteristics of 24 martens that were radio-tracked and snow-tracked between August 2002 and March 2004. From these data we developed 2 resource selection functions, one for summer and the other for winter, using logistic regressions. In summer, selected sites were mainly characterized by abundant biomass of spruces and short (≤30 cm) herbaceous plants and low biomass of tall (>30 cm) herbaceous plants. Other factors, such as increased coniferous canopy closure and amount of coarse woody debris (CWD) and reduced lateral cover (LC5) were included in the composite model. In winter, sites with closed coniferous canopy and LC5, high snow sinking depth, greater amounts of CWD, greater basal area, and greater tree density were more likely to be visited by marten. These variables may be related to 3 factors that play roles in marten ecology: prey abundance, protective cover, and thermoregulation. Our results also show that, unlike clear-cutting with protection of regeneration and soils (CPRS) and pre-commercial thinning (PCT), partial logging techniques (PL) could maintain, under certain conditions, the structural elements required by pine marten in a managed forest. These elements would favour prey abundance and detection, protective cover, and rest and thermoregulation sites.
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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 | 0.000 |
| 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 | 0.000 | 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".