Improving Rietveld‐Based Clay Mineralogic Quantification of Oxisols Using Siroquant
Bibliographic record
Abstract
Although mineralogic quantitative phase analysis (QPA) of the soil clay fraction can provide useful information for the improvement of soil management practices, QPA often requires a combination of several analytical techniques, which can be expensive and time consuming. One alternative that involves a single analysis to give accurate QPA of soils is the use of the Rietveld method to analyze powder x‐ray diffraction (XRD) data. In this study, we evaluated the accuracy of the XRD–Rietveld approach for mineralogic quantitative analyses of Oxisol clays when observed structure factors [ F ( hkl )] of pedogenic minerals (i.e., kaolinite, hematite, and goethite) are used in the Rietveld analyses performed using Siroquant software. The results showed that although the structures of disordered clay minerals are especially difficult to incorporate in standard Rietveld refinement, which relies on ordered three‐dimensional structure models, Mineralogic quantification can be accurately done for complex matrices having a large number of phases and various degrees of structural and compositional disorders when observed F ( hkl ) values are used. It is also possible to develop observed F ( hkl ) values for Al‐hematite and Al‐goethite from impure samples of such oxides to use as starting structure models for Rietveld analysis. We believe that this approach can be successfully extended to other geologic materials.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".