Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra
Bibliographic record
Abstract
Spectral analysis is a useful tool for the rapid and accurate prediction of soil properties. Our objective is to select the best model for predicting the three soil cation concentrations ([Na+], [Mg2+], and [Ca2+]) and sodium adsorption ratio (SAR). Three methods were applied, i.e., stepwise multiple linear regression (SMLR), partial least-squares regression (PLSR), and support vector machine (SVM). Estimation models for four soil properties were developed using three different spectral processing and transformation techniques, i.e., reflectance (Re), logarithm of reciprocal Re (LR), and standard normal variable of Re (SNV) were used. A total of 36 models were established. Of these, 27 models for [Na+], [Mg2+], and [Ca2+] were not applicable for subsequent prediction, because the coefficients of determination (R2) were not high (0.224–0.689), and their relative percent deviations (RPD) were all smaller than the 1.4 threshold. However, the models for SAR~R using PLSR (R2 = 0.728 for calibration and 0.661 for validation, RPD = 1.43), SAR~LR using SVM (R2 = 0.791 for calibration and 0.712 for validation, RPD = 1.81), and SAR~SNV using SVM (R2 = 0.878 for calibration and 0.814 for validation, RPD = 2.13) were valid for further prediction. Finally, SAR~SNV using SVM was selected as the best model. There are intrinsic factors resulting in an unsatisfied model performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".