Soil organic carbon measurements influence FT‐NIR model training in calcareous soils of Saskatchewan
Bibliographic record
Abstract
Abstract This study compares acid digestion and temperature ramping methods for obtaining soil organic carbon (SOC) reference data to train Fourier transform near infrared (FT‐NIR) models in carbonate‐rich Saskatchewan agricultural soils. FT‐NIR spectra were measured on soil samples ( n = 431) from carbonate‐rich Dark Brown Chernozem soil, with quantification of inorganic and organic carbon. Spectra were transformed using continuous wavelet transform and analyzed using cubist regression tree models. Models were built using a 70:30 train test split validation approach. Spectral feature selection, wavelet scale, and model and hyperparameter optimization were conducted using fivefold cross‐validation analysis on the training dataset. All validation metrics were calculated using the testing dataset. The temperature ramping method identified outliers with soil inorganic carbon (SIC) greater than 1.5%, which were not detected using the acid digestion method. SOC and SIC prediction accuracy was higher using temperature ramping data (coefficient of determination: R 2 = 0.66 and 0.63, Lin's concordance: ccc = 0.78 and 0.77) compared to acid digestion data ( R 2 = 0.44 and 0.42, ccc = 0.64 and 0.62). Total carbon (TC) prediction accuracy was similar for both methods ( R 2 = 0.58, ccc = 0.71). Removing samples with high carbonate (SIC > 1.5%) improved SOC and TC prediction accuracy using temperature ramping data ( R 2 = 0.70, ccc = 0.81 for SOC; R 2 = 0.64, ccc = 0.75 for TC) but not when using acid digestion method. This study suggests that high carbonate content may negatively affects SOC model accuracy, especially when relying upon acid digestion methods for reference SOC data.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 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 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".