Contributions of harvest slash to maintaining downed woody debris in selection-managed forests
Bibliographic record
Abstract
Harvest slash can represent a major source of downed woody debris (DWD) in selection-managed forests. In this study, we analyze the volume, cover, size distribution, and decay-class distribution of DWD input by selection harvesting in central Ontario, Canada. Selection harvesting input 23.9 m3 DWD·ha–1 (0.013 m2 DWD·m–2), with cut basal area explaining 46% and 30% of the respective within-stand variation in cover and volume, respectively. The size distribution of the slash was similar to that of DWD in permanent sample plots (including old-growth stands and stands that have not been recently harvested), countering a common assumption that harvesting inputs only small-sized material. Harvest-origin DWD was bimodally distributed across decay classes, with the first peak (decay class 1) associated with fresh harvest slash and a second smaller peak (decay class 3) likely representing dead trees and branches that were felled or broken during harvest operations. A matrix projection model showed that slash can maintain DWD levels in managed, uneven-aged stands comparable with those in unmanaged stands, but the mean decay class increases steadily over a 20-year period after harvest. Our results underline the importance of harvest inputs for maintaining DWD pools in selection-managed forests and provide baseline information against which to compare forests managed with higher utilization standards.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".