Sources and stability of particulate organic matter (POM) and mineral-associated organic matter (MAOM) on the Loess Plateau: Implications for soil carbon management
Bibliographic record
Abstract
The sources and stability of particulate organic matter (POM) and mineral-associated organic matter (MAOM) fundamentally determine soil carbon dynamics, yet their characteristics across different land use types remain disputed. We investigated the sources and stability of POM and MAOM across afforested land, grassland, and abandoned cropland on China’s Loess Plateau using biomarker approaches (lignin phenols and amino sugars) in combination with 13 C nuclear magnetic resonance spectroscopy, and analyzed the primary factors influencing them through measuring soil physicochemical properties and microbial communities. Results showed that across all land use types both POM and MAOM were predominantly derived from microbial residues rather than plant inputs, with amino sugar to lignin phenol ratios exceeding 1.5. POM was more stable than MAOM in all land use types, even though MAOM had a significantly higher ratio of alkyl C/O-alkyl C (1.01) than POM (0.84). In all land use types, POM and MAOM in croplands had the lowest sources of lignin and amino sugars, with 47.62 mg kg ‑1 and 75.08 mg kg ‑1 for the former, respectively, and 18.77 mg kg ‑1 and 88.83 mg kg ‑1 for the latter, respectively, and their stability were poorest. The sources of POM were primarily influenced by belowground biomass, whereas those of MAOM were mainly regulated by soil physical properties and bacterial communities. Interestingly, soil total nitrogen emerged as the dominant factor controlling the stability of both fractions. These findings underscore the importance of developing site-specific tailored carbon management strategies, especially POM management strategies, to achieve nature-based solutions to global climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".