Chemical transformations of deadwood and foliar litter of mixed boreal species during decomposition
Bibliographic record
Abstract
Deadwood constitutes an important input of carbon to soil, but its role in carbon sequestration over the long term is not well documented in the eastern boreal forests of Canada, especially when compared with foliar litter. The objectives of this study were to characterize and compare patterns of mass loss and changes in chemical composition of deadwood and foliar litter of trembling aspen (Populus tremuloides Michx.), white spruce (Picea glauca (Moench) Voss), and balsam fir (Abies balsamea (L.) Mill.) during a 5- to 6-year period of field decomposition, using litterbags, solid-state 13C nuclear magnetic resonance analysis, and lignin monomer quantification by cupric oxide oxidation. The maximum decomposition limit was similar between foliar litter and wood material, but foliar litter decomposed faster, reached the estimated maximum decomposition limit, and converged to a composition rich in alkyl, phenolic, and carbonyl carbon. However, wood did not reach the estimated maximum decomposition limit and underwent relatively little chemical changes, remaining with high carbohydrate content. At the end of the experiment, aspen wood still had a lower lignin concentration than that of conifers, but contained higher proportions of alkyl and carbonyl carbon. Although wood contributes to a greater diversity in the chemical composition of the forest floor, foliar litter, which keeps a high alkyl C content throughout its decay, could generate more recalcitrant residual organic matter.
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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".