Fluorescent properties of water-extractable organic matter in low-gradient, clay soils offer insight for management and restoration
Bibliographic record
Abstract
Soil organic matter (SOM) is an important component of soil fertility and ecosystem health, and a key store of terrestrial carbon. Given that analytical approaches are cost-prohibitive and labour-intensive, we applied ultraviolet–visible (UV–Vis) and fluorescence spectroscopy across a gradient of agricultural practices and restoration. We evaluated the relationships between land management, soil organic carbon (SOC), and properties of water-extractable organic matter (WEOM). The quantity of SOC and aromatic abundance of WEOM was found to be correlated to land management (agricultural or forested) and soil depth (0–60 cm). SOC levels in reforested sites were nearly twice as high as agricultural sites. Significantly higher levels of absorbance were found in reforested sites; other differences in WEOM composition were not found. Neither SOC quantity nor WEOM composition differed among agricultural practices, suggesting that SOC accumulation under alternative practices may be slow, and result in high organic matter turnover rates that maintain similar SOC levels. Differences between land management types were limited to shallow soil layers (0–15 cm) only. At all sites, clay soils displayed: strong vertical zonation of SOC, poorly drained shallow A horizons with accumulated organic matter, deeper impermeable clay mineral horizons with lower concentrations of SOC, and, more protein-like WEOM composition. These results suggest that reforestation of post-agricultural clay soils is associated with increased SOC, though restoration of WEOM composition to forest-like levels may require multidecadal time scales. WEOM optical properties may be slower to reflect management changes than total SOC accumulation, indicating the importance of tracking organic matter quality and composition to fully understand soil health.
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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 | 0.000 | 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 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".