Use of constructed coarse woody debris corridors in a clearcut by American martens (Martes americana) and their prey
Bibliographic record
Abstract
American martens (Martes americana) are typically associated with mature coniferous forests because they provide overhead cover and coarse woody debris (CWD) that martens require for protection and hunting. Therefore, clearcuts are considered poor marten habitat because they contain no overhead cover and relatively little CWD. We examined the efficacy of retaining CWD and constructing CWD corridors in a recently harvested clearcut to promote the use of the area by martens and small mammals, a major prey resource. We installed remote cameras in corridors, the surrounding clearcut and forest to monitor the distribution of martens and small mammals. Martens and red squirrels did not use CWD corridors more frequently than clearcut areas in summer; forest-floor small mammals, however, used corridors approximately three times as frequently as other habitats (x2 = 13.374, P = 0.001). Marten presence was positively associated with mature, dense forest and long pieces of CWD. In winter, red squirrels used corridors more frequently than other clearcut areas, and limited data suggested that martens preferred the corridors to other clearcut areas. Consequently, we recommend that forest managers retain CWD and construct CWD corridors within clearcuts to provide small mammal habitat, and to enhance marten 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".