Contrasting downed woody debris dynamics in managed and unmanaged northern hardwood stands
Bibliographic record
Abstract
The reported effects of selection silviculture on downed woody debris (DWD) vary. To investigate the processes underlying potential management impacts on DWD stocks and fluxes, we conducted a repeated census of downed wood in selection-harvested, selectively harvested, and unmanaged (old-growth) stands in central Ontario. DWD was significantly more abundant in stands harvested within the last 20 years than in stands harvested earlier, and shifted towards more advanced decay classes over the first 20 years after harvest. These results are consistent with persistence of a harvest-related DWD pulse for up to two decades in managed stands. The transition of DWD from early and middle decay classes to more advanced decay classes proceeded more slowly in managed than unmanaged stands. Species type, identity of fungal fruiting bodies, presence of a cut surface, and plot moisture class were significant predictors of variation in decay dynamics within particular decay classes; however, these factors did not account for observed differences in decay-class transitions between managed and unmanaged stands. A decay class matrix model projected DWD half-lives of 19 years for unmanaged stands and 21 years for managed stands. Over the long term, slower decay dynamics may help somewhat in maintaining relatively high DWD abundances in stands managed under selection silviculture.
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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 | 0.000 |
| 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) | 0.000 | 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".