Vis‐Near IR Reflectance Spectroscopy for Soil Organic Carbon Content Measurement in the Canadian Prairies
Bibliographic record
Abstract
The objective of this study was to measure soil organic carbon (SOC) content for Canadian prairies soils using Vis‐near IR reflectance (VisNIR) spectroscopy. Specifically, the effects of the spectral data pretreatment method and number of latent variables on SOC prediction were determined. In addition, the capability of VisNIR spectroscopy to capture SOC variability was evaluated. For these purposes, 491 soil samples of 0–30 cm from two perpendicular 1600 m long sampling transects (NS and WE transects with 239 and 252 samples, respectively) in the Canadian prairies were scanned by VisNIR spectroscopy. SOC content at one transect was predicted by models calibrated at the other transect. The potential of VisNIR spectroscopy in predicting SOC was verified in this area. Smoothing using cubic smoothing spline outperformed other pretreatment methods. The performance of SOC prediction improved and then worsened with the increase in the number of latent variables in the models. Wavelet transform indicated a similar pattern of high variances in the scale‐location domains between the predicted and measured SOC; the predicted SOC showed a decreased structured variability at the NS transect and increased nugget effect at the WE transect. At both transects, weaker, but with the same levels, spatial dependency and greater correlation length were observed for the predicted SOC compared with the measured SOC. The obtained results can be applicable for SOC measurements with VisNIR spectroscopy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".