Fine-scale selection by marten during winter in a young deciduous forest
Bibliographic record
Abstract
American marten (Martes americana (Turton, 1806)) are often associated with old-growth forests, but have been detected living in a young deciduous forest in northern British Columbia, where a previous coarse-scale analysis failed to detect significant habitat selection. To address this paradox, we examined fine-scale habitat selection for specific activities. We used radiotelemetry and snowtracking to identify sites that appeared to have been used for resting, foraging, scent marking, and traveling during the winters of 19981999 and 19992000. Then we conducted vegetation surveys at these activity sites and at nearby random locations and used logistic regression to measure selection. Based on the number of significant variables and model fit, we detected more selectivity by marten for resting than for foraging and scent-marking sites, and no selectivity for traveling. Marten exhibited selection for several habitat structures that are characteristic of older forests (e.g., rootballs and wide-diameter snags), but that can be retained in some manipulated forests. With the exception of wide-diameter snags (selected at both resting sites and scent marks), marten selected different habitat structures for each type of activity. These results may help to explain why marten are able to survive in this and other sites that provide seemingly unsuitable habitat.
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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 | 0.000 | 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".