Land-Use Change and Environmental Properties Alter the Quantity and Molecular Composition of Soil-Derived Dissolved Organic Matter
Bibliographic record
Abstract
Dissolved organic matter (DOM) is a major pool of actively cycling organic carbon in soils, and can be exported to aquatic environments. The quantity and chemistry of DOM vary with different environmental factors, such as soil properties and climate. Accordingly, the amount and composition of DOM in soil and that which is exported to aquatic systems are likely altered by land-use change, but these aspects have not been studied with various environmental factors and associated land-use gradients in detail. To address this, both native and cultivated soil samples from North and South America were used to extract soil-derived DOM. DOM samples were isolated and analyzed for total organic carbon concentration and solution-state nuclear magnetic resonance spectroscopy. The concentration of dissolved organic carbon (DOC) was significantly correlated with soil organic carbon concentration (r = 0.869, p < 0.01, n = 14) and relative soil organic matter degradation state (r = −0.578, p < 0.05, n = 14). Land-use change decreased the controls of environmental factors on the concentration of DOC but enhanced the correlations between DOM aromaticity and mean annual precipitation (r = −0.957, p < 0.01, n = 7) and sand concentration (r = −0.867, p < 0.05, n = 7). This study found that land-use conversion and environmental conditions alter the quantity and quality of DOM distinctly and uniquely. Here, we identified the main factors that regulate the formation of soil DOM and its potential mobility in soil profiles as well as export to aquatic ecosystems.
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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) | 0.000 | 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 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".