Infrared spectroscopy prediction of organic carbon and total nitrogen in soil and particulate organic matter from diverse Canadian agricultural regions
Bibliographic record
Abstract
Infrared spectroscopy has the capacity to predict soil organic carbon (SOC) and total nitrogen (TN) at local/regional scales, but no studies have been conducted to evaluate this technique at a large (cross-regional) scale in Canada. In this paper, mid-infrared (MIR) and near-infrared (NIR) spectroscopies in combination with partial least-squares regression (PLSr) were used to predict SOC and TN in whole soil and in particulate organic matter (POM) fractions on cross-regional, regional, and local scales. Both MIR- and NIR-PLSr models have well estimated SOC [coefficient of determination (R2) = 0.79–0.92, residual prediction deviation/ratio of prediction to deviation (RPD) = 2.19–3.47], TN (R2 = 0.70–0.92; RPD = 1.83–3.50), POM-C (R2 = 0.76–0.96; RPD = 2.04–5.25), and POM-N (R2 = 0.70–0.97; RPD = 1.83–5.78). The prediction efficiency of cross-regional models (R2 = 0.90–0.96; RPD = 3.13–5.49) was similar to or better than the prediction of regional (R2 = 0.70–0.97; RPD = 1.83–5.78) and local models (R2 = 0.70–0.96; RPD = 1.83–5.33) and overall MIR-PLSr models (R2 = 0.90–0.96; RPD = 1.98–3.47) yielded similar predictions for SOC and TN relative to NIR-PLSr models (R2 = 0.70–0.92; RPD = 1.83–3.50) at cross-regional scale. Hence, it may be possible to develop MIR and (or) NIR spectral models to estimate and monitor SOC, TN, POM-C, and POM-N, and therefore, soil quality, in a rapid and cost-efficient manner across regions with diverse soil types, climate, and cropping history.
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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 | 0.000 | 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 | 0.001 |
| 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 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".