Effect of thinning and prescribed fire restoration treatments on woody debris and snag dynamics in a Sierran old-growth, mixed-conifer forest
Bibliographic record
Abstract
Forest managers have little information of the effects of common restoration treatments, thinning and burning, on dead woody material (DWM) dynamics in fire-suppressed forests. Fine woody debris (FWD; 0.6–29.9 cm), coarse woody debris (CWD; ≥30.0 cm), and snags (≥5 cm) were inventoried and mapped in eighteen 4 ha plots before and after applying thinning (overstory, understory, and no thinning) and burning (burn and no burn) treatments. The combination of burning and thinning reduced FWD and CWD quantity and mean piece size, removed highly decayed logs, and increased small (5.0–24.9 cm) snag recruitment. In contrast, thin-only treatments produced similar results but increased FWD and did not remove many small snags. There were no differences in DWM response between the two thinning treatments. Log and snag spatial patterns prior to and following treatment were similar. These results indicate that burning in combination with thinning is more effective at reducing surface FWD and CWD, and removing small trees than are burn-only and thin-only treatments. Although large snags and logs were consumed in the burn, long-term recruitment of these habitat structures relies on managers retaining large-diameter trees. Repeated burns need to be conducted after initial restoration treatments to understand natural patterns of DWM.
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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 | 0.001 | 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".