Density-dependent woody detritus accumulation in an even-aged, single-species forest
Bibliographic record
Abstract
Deadwood in forests influences fire intensity, stores carbon and nutrients, and provides wildlife habitat. We used a 54-year-old density management experiment in Larix occidentalis Nutt. forests to evaluate density dependence of woody detritus accumulation. Based on self-thinning theory, we expected woody detritus produced by the current stand to increase with stand density. Density-dependent woody detritus accumulation was apparent for fine woody debris and snags and for all woody detritus pools combined. Clear size–density relationships were apparent for coarse woody debris (CWD) and snags; mean piece size decreased with increasing stand density. Legacy CWD that originated from the preharvest old-growth stands accounted for about 45% of total woody detritus biomass. Live trees were largest in the low-density thinning treatments. Greater woody detritus biomass in the high-density and unthinned treatments originates primarily from past self-thinning, with additional inputs from density-dependent top breakage due to snow and ice and branch self-pruning. Because our results were driven by self-thinning mortality, the general trend of increasing woody detritus accumulation with increasing stand density should hold for maturing even-aged stands in other cool temperate and boreal forests.
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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 | 0.000 |
| 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".