Analysis of soil organic matter biomarkers by sequential chemical degradation and gas chromatography – mass spectrometry
Bibliographic record
Abstract
Low molecular weight (LMW) biomarkers can be used to trace the source and stage of soil organic matter. However, methods that selectively isolate these groups of compounds are underdeveloped. In this study, we isolate biomarkers by a successive series of extraction and chemical degradation procedures involving solvent extraction (TSE), base (BHY) and acid hydrolysis (AHY), and CuO oxidation (CUO). GC-MS was used to analyze these fractions and the extraction methods were verified by solid-state 13C NMR spectroscopy. The GC-MS response was high for the BHY products (96%), intermediate for the TSE (30%) and CUO (19%), but very low for the AHY fraction (5%) indicating that the fractions contain polar or high molecular weight compounds. Aliphatic lipids (62%), phenols and benzyls (17%) were the predominant classes, accompanied by minor abundances of mono- and disaccharides, LMW acids, terpenoids, steroids, amino acids, and amino sugars. The TSE and BHY fractions contained mainly aliphatic lipids derived from plant waxes, cutin, and suberin. Lignin-derived phenols are the major products in the CUO fraction, and amino compounds and carbohydrates of various sources were identified in the AHY products. The sequential degradation method is useful for the isolation and identification of apolar, LMW biomarkers in soil.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".