Decomposition and change in N and organic composition of small-diameter Douglas-fir woody debris over 23 years
Bibliographic record
Abstract
Managing second-growth forests for multiple benefits requires enhanced information on decomposition of woody debris generated during forestry operations. Experimental thinning and biomass sampling at the Shawnigan Lake Research Forest on southern Vancouver Island facilitated retrospective sampling of Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) stem sections (4.9–21.5 cm diameter) from 0 to 23 years after cutting. A sigmoidal pattern of density loss was observed, while a single-exponential fit gave a decay rate (k) of 0.058 year–1. Initial N concentrations were higher in bark than in sapwood and heartwood, and all increased during decay, while C/N ratios declined to around 175 for wood and 70 for bark. Nitrogen contents initially increased but tended to decline after about 50% C loss. Solid-state 13C nuclear magnetic resonance spectroscopy showed little change in organic composition, consistent with white-rot fungal decay, with only a few 19-year samples showing large increases in lignin typical of extensive brown-rot decay. Hydrolyzable monosaccharides of wood and bark were dominated by glucose followed by mannose, xylose, galactose, and arabinose. For wood, the mole fraction of mannose decreased with decomposition, whereas those of glucose, arabinose, and galactose increased slightly and that of xylose remained constant for both brown and white rot. Our results support indications that decomposition of smaller diameter woody debris from managed conifer forests will likely be dominated by white-rot fungi, leaving residues high in cellulose rather than in lignin.
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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 | 0.001 | 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) | 0.000 | 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".