Decomposition, δ<sup>13</sup>C, and the “lignin paradox”
Bibliographic record
Abstract
The natural abundance of 13C (δ13C) generally increases with decomposition of organic matter. This is contrary to the expected decrease, as lignin is hypothesized to accumulate relative to isotopically heavier cellulose. Our objective was to test the hypothesis that 13C depletion should be observed for gymnosperm logs that typically develop advanced brown-rot decay with high lignin content. With increasing lignin concentration [previously determined by nuclear magnetic resonance (NMR)], δ13C tended to become more negative for samples of Pseudotsuga menziesii, Tsuga heterophylla, Thuja plicata, and unidentified species from Coastal Forest Chronosequence sites of southern Vancouver Island. For a larger sample set without NMR analysis, δ13C was significantly more depleted for the highest decay classes, and total C was negatively correlated with δ13C, consistent with the higher total C of lignin than of cellulose. Relationships of total C and δ13C with density were much weaker. We discuss causes for the variability of δ13C in coarse woody debrisfrom these sites, and how the apparent paradox in the predicted change of δ13C with decomposition is largely due to the confusion of lignin, the biopolymer produced by higher plants, with the acid-unhydrolyzable residue (AUR) of the proximate analysis procedure commonly used to assess litter quality in decomposition studies. Key words: Coarse woody debris, decomposition, lignin, 13C NMR, δ13C, proximate analysis
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".