Influence of coarse wood and fine litter on forest organic matter composition
Bibliographic record
Abstract
Forest soil organic matter (SOM) is affected by inputs from coarse wood (CW) and fine litter (FL, e.g., leaves, twigs, cones, and needles). The influence of these materials on forest SOM was studied in a lodgepole pine (Pinus contorta) forest in southeastern Wyoming. Organic materials in CW sites were significantly (P < 0.05) more acidic, but contained half the total N of FL sites. Forest floor materials and SOM C contents were significantly greater in CW samples. Lignin decomposition products (CuO analysis) accumulated primarily in organic horizons of both sites, with significantly higher (60–70%) contents in CW materials. Vanillyl compounds were the primary lignin products from both sites, with cinnamyl compounds also important in SOM from FL sites. Vanillic acid to vanillin ratios were significantly higher in mineral soils under CW. 13C-NMR spectra indicated CW materials were enriched in lignin, and that humic acids from both site types were very similar and unusually high in alkyl C. Fulvic acids were also high in O-alkyl and carboxyl C, particularly in the CW sites. Results suggest there are differences in forest C constituents and that removal of CW could possibly alter forest soil dynamics that would impact forest productivity and biodiversity. Key words: Forest, coarse wood, litter, carbon, soil organic matter, humic substances
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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".