Diversifying clearcuts with green-tree retention and woody debris structures: conservation of mammals across forest ecological zones
Bibliographic record
Abstract
We tested the hypotheses (H) that on newly clearcut-harvested sites, (H1) abundance and species diversity of the forest-floor small mammal community, and (H2) abundance, reproduction, and recruitment of red-backed voles ( Vigors), would increase with higher levels of structural retention via green-tree retention (GTR) and woody debris (dispersed and constructed into windrows). Study areas were located in three forest ecological zones in southern British Columbia, Canada. For H1, mean total abundance did generally increase with the gradient of retained habitat structure. Mean species richness and diversity were similar among treatment sites but did show an increasing gradient with structural compexity. For H2, mean abundance, reproduction, and recruitment of were higher in GTR and windrow sites than those without retained structures. There was a positive relationship between mean abundance of and total volume of woody debris across treatments. This study is the first investigation of the responses of forest-floor small mammals to an increasing gradient of retained habitat structure via GTR and woody debris on clearcuts. Our assessment of a combination of these two interventions suggested a potentially strong additive effect that could be cautiously extrapolated across three forest ecological zones. With the advent of low levels of GTR on clearcuts, woody debris structures should help provide some habitat to conserve forest mammals on harvest openings.Myodes gapperiM. gapperiM. gapperi
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".