Residual forest structure influences behaviour of Pacific marten (Martes caurina) on post-fire landscapes
Bibliographic record
Abstract
Wildfires are broad-scale disturbances in North American forests, with impacts that persist for many decades. Further disturbance from post-fire salvage logging is extensively modifying burned landscapes. The removal of habitat structure by fire and salvage logging may affect the persistence of forest-specialist wildlife such as Pacific marten (Martes caurina). However, it is unclear which resources are important to marten on burned landscapes. We used snow tracking and habitat surveys to examine marten habitat selection after three large fires in north-central Washington, USA (10–13 years post-fire), and central British Columbia, Canada (1–2 and 6–9 years post-fire). We developed site-scale habitat models to explain marten foraging and scent-marking post-fire, and assessed further structural changes from salvage logging. Foraging marten chose sites with lower burn severity, greater canopy closure, more vertical structures (trees, snags, saplings, and shrubs), and greater moss/lichen cover than what was generally available. When scent-marking, marten selected structurally-complex sites with abundant deadfall or saplings. Marten moved more quickly when canopy cover was sparse, and rarely used salvage-logged areas. Our results suggest that marten rely on residual habitat structure within large burns, and that secondary disturbance from salvage logging is substantially more harmful to marten than the original fire.
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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 | 0.001 | 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".