Near-infrared spectroscopic assessment of hot water extractable and oxidizable organic carbon in cultivated and uncultivated Mollisols in China
Bibliographic record
Abstract
Abstract The hot water extractable organic carbon (HWEOC) and K2Cr2O7 oxidizable organic carbon (OOC) have been suggested as indicators to assess soil management effects on soil organic matter; however, traditional methods for measuring these C fractions are costly and tedious. The potential of using near-infrared reflectance spectroscopy (NIRS) with partial least squares (PLS) regression to predict HWEOC and OOC concentrations in cultivated and uncultivated Mollisols in China were explored in this paper. The soil organic carbon (SOC), OOC, and HWEOC in 0–30 cm layer were 37.6, 41.2, and 58.8% lower in cultivated than in uncultivated soils. The HWEOC is more sensitive to soil management relative to SOC or OOC. HWEOC concentrations were accurately predicted using NIRS-PLS model, with high coefficient of determination (R 2=0.89), residual prediction deviation (RPD=3.69) for model calibration, and high R 2 (0.85), RPD (3.03), and correlation coefficient (r=0.92) of predicted and measured values in the validation set. Excellent prediction for OOC was acquired with R 2 and RPD at 0.97 and 6.11 for model calibration, respectively, and R 2 and RPD and r at 0.92, 5.75, and 0.97 for model validation, respectively. This study indicated that the HWEOC could be used to illustrate the impacts of agronomic management on soil quality. Both of HWEOC and OOC can be accurately quantified using NIRS-PLS approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".