Infrared spectroscopy estimation methods for water-dissolved carbon and amino sugars in diverse Canadian agricultural soils
Bibliographic record
Abstract
Infrared spectroscopy has the potential to rapidly analyse soil water-dissolved carbon and amino sugars. In this study, mid-infrared (MIR) and near-infrared (NIR) spectra collected from soil water extracts or from bulk soils were analysed with partial least squares regression (PLSr) to estimate the concentrations of water-dissolved carbon and amino sugars in diverse agricultural soils collected from five field sites in two western and two eastern Canadian provinces. The MIR-PLSr models developed from soil water extract spectra estimated hot-water (100 °C) dissolved carbon (HWDC) [R2 = 0.97–0.70, ratio of prediction to deviation (RPDp) = 6.13–1.83] well, but the MIR-PLSr models did not estimate cold-water (21 °C) dissolved carbon (CWDC) well (R2 = 0.82–0.50, RPDp = 2.35–1.42). The model estimates of HWDC at the multisite scale (all samples together) and for the two western Canada sites (R2 = 0.97–0.93, RPDp = 6.13–3.68) surpass the modal estimates for the three eastern Canadian sites (R2 = 0.81–0.70, RPDp = 2.28–1.83). The MIR- and NIR-PLSr models derived from bulk soil spectra both estimated HWDC well at the multisite scale (R2 = 0.91–0.88, RPDp = 3.32–2.90) and for the western Canada sites (R2 = 0.90–0.87, RPDp = 3.18–2.96). Models developed from hot-water extract spectra and bulk soil spectra resulted in poor estimates of soil amino sugars (R2 = 0.74–0.21, RPDp = 1.99–1.12), except for the approximate quantitative estimation of muramic acid by models based on soil spectra at the western and the multisite scale (R2 = 0.82–0.80, RPDp = 2.33–2.21). We concluded that MIR and NIR models at regional and multisite scales can be used as a tool to monitor HWDC but that additional research is required for estimating soil amino sugars.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".