Rubidium measured by XRF as a predictor of soil particle size in limestone and siliceous parent materials
Bibliographic record
Abstract
Abstract Purpose Information about particle size distribution (PSD) and soil texture is essential for understanding soil drainage, porosity, nutrient availability, and trafficability. The sieve-pipette/gravimetric method traditionally used for particle size analysis is labour-intensive and resource-intensive. X-ray fluorescence (XRF) spectrometry may provide a rapid alternative. The study’s aim was to examine the use of XRF for rapid determination of PSD in Irish soils. Methods Soils (n = 355) from existing archives in Ireland were analysed with a benchtop energy-dispersive XRF (EDXRF). Correlation and regression analyses were determined to compare Rb, Fe, Al, and Si concentrations to % clay, % silt, and % sand. Also, linear regression models were developed to compare % clay, % sand, and % silt measured by the gravimetric method to values predicted by EDXRF. Results The relationship between element concentration and PSD was dependent on parent material. Rb, Al, and Fe showed a significant (p < 0.05) correlation (r > 0.50) with % clay and % sand in soils derived from limestone and siliceous stone parent materials. Rb was the best predictor for % clay (R2 = 0.49, RMSE = 10.20) in soils derived from limestone and siliceous stone-derived soils. Conclusion Geochemistry and clay mineralogy of the soils’ parent material strongly influenced the EDXRF’s ability to predict particle size. The EDXRF could predict % clay in soils from parent materials which weather easily, but the opposite was true for soils with parent material recalcitrant to weathering. In conclusion, this study has shown that the EDXRF can screen % clay in soils derived from limestone and siliceous stone parent 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".