Determination of organic carbon and nitrogen in particulate organic matter and particle size fractions of Brookston clay loam soil using infrared spectroscopy
Bibliographic record
Abstract
The objective of this study was to determine whether models developed from infrared spectroscopy could be used to estimate organic carbon (C) content, total nitrogen (N) content and the C:N ratio in the particulate organic matter (POM) and particle size fraction samples of Brookston clay loam. The POM model was developed with 165 samples, and the particle size fraction models were developed using 221 samples. Soil organic C and total N contents in the POM and particle size fractions (sand, 2000–53 µm; silt, 53–2 µm; clay, <2 µm) were determined by using dry combustion techniques. The bulk soil samples were scanned from 4000 to 400 cm −1 for mid‐infrared (MIR) spectra and from 8000 to 4000 cm −1 for near‐infrared (NIR) spectra. Partial least squares regression (PLSR) analysis and the ‘leave‐one‐out' cross‐validation procedure were used for the model calibration and validation. Organic C and N content and C:N ratio in the POM were well predicted with both MIR‐ and NIR‐PLSR models ( = 0.84–0.92; = 0.78–0.87). The predictions of organic C content in soil particle size fractions were also very good for the model calibration ( = 0.84–0.94 for MIR and = 0.86–0.92 for NIR) and model validation ( = 0.79–0.94 for MIR and = 0.84–0.91 for NIR). The prediction of MIR‐ and NIR‐PLSR models for the N content and the C:N ratio in the sand and clay fractions was also satisfactory ( = 0.73–0.88; = 0.67–0.85). However, the predictions for the N content and C:N ratio in the silt fraction were poor ( = 0.23–0.55; = 0.20–0.40). The results indicate that both MIR and NIR methods can be used as alternative methods for estimating organic C and total N in the POM and particle size fractions of soil samples. However, the NIR model is better for estimating organic C and N in POM and sand fractions than the MIR model, whereas the MIR model is superior to the NIR model for estimating organic C in silt and clay fractions and N in clay fractions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".