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) [R 2 = 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 (R 2 = 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 (R 2 = 0.97–0.93, RPDp = 6.13–3.68) surpass the modal estimates for the three eastern Canadian sites (R 2 = 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 (R 2 = 0.91–0.88, RPDp = 3.32–2.90) and for the western Canada sites (R 2 = 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 (R 2 = 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 (R 2 = 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".