Near‐Infrared Reflectance Spectroscopy Prediction of Soil Properties: Effects of Sample Cups and Preparation
Bibliographic record
Abstract
Most methods for soil analysis are based on wet chemistry. Near infrared reflectance spectroscopy (NIRS) is a cost‐effective and environmentally sound alternative technique. This study evaluated the effect of sample fineness (0.2, 0.5, 1, and 2 mm) and sample cups (transport versus spinning) on the accuracy of NIRS predictions of soil texture, cation‐exchange capacity, pH, total C and N, organic C, and potentially mineralizable N (N min ) using 150 air‐dried samples collected from a 15‐ha site dominated by Humaquept, Endoaquept, and Dystrochrept soils. The best spectral pretreatment was determined for each property. Principal component analysis (PCA) was used to select samples in calibration and validation sets. Calibration equations were developed using the modified partial least square regression. The accuracy of NIRS prediction was evaluated using three statistics for the prediction set: coefficient of determination ( R 2 ), ratio of performance deviation (RPD), and ratio error range (RER). Across the factorial designed treatments, successful calibrations were observed for clay, sand, and N min ( R 2 ≥ 0.90, RPD ≥ 3, RER ≥ 15). Prediction accuracy of pH was poor (0.51 ≤ R 2 ≤ 0.74, 1.39 ≤ RPD ≤ 1.92, 6.13 ≤ RER ≤ 8.33), while it was intermediate for remaining properties. Sample fineness of 2 mm appeared to be sufficient since finenesses of 0.2, 0.5, or 1.0 mm did not improve calibration accuracy. These findings at small scale should not be extrapolated and further investigations are required to validate them at a larger scale.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".