Analysis of Molecular Structure Changes in Humic Acids from Manure-Amended Soils over 17 Years Using Elemental Analysis and Solid-State 13C Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
Soil organic matter (SOM) plays an important role in regulating plant nutrient availability. Here, the effects of the long-term application of different forms of processed swine manure on the SOM structure are explored through the analysis of humic acid (HA) using elemental analysis and 13C solid-state nuclear magnetic resonance (NMR) spectroscopy. The HAs from soils amended with liquid swine manure (LSM) and swine manure compost (SMC) are found to be more humified compared to the soils treated with solid swine manure (SSM) and the control (CK). The H/C and O/C molar ratios suggest that carboxyl-rich aliphatic structures are the most important class of biomolecules contributing to the LSM- and SMC-HA structures, while lignin-like structures are the most important biomolecules contributing to the CK- and SSM-HAs. SSM promoted the formation of aliphatic polar structures, which are more susceptible to aerobic biodegradation, whereas the CK facilitated the inclusion of condensed aromatic structures into the HA. Apart from the LSM-HA, the proportion of carboxylic acid functional groups reduced with manure application, while the proportion of phenolic acid functional groups increased. LSM-HA has the highest potential to enhance plant nutrient availability.
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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 | 0.001 |
| 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".