A simple method for quantitative analysis of elements by WD‐XRF using variable dilution factors in fusion bead technique for geologic specimens
Bibliographic record
Abstract
A common approach in the quantitative analysis of geological samples by X‐ray fluorescence is to establish calibration lines for elements of interest by using several reference materials (RMs) and/or the combination of RMs and pure chemicals. Herein, we introduce an alternative to use only two RMs, to establish a calibration application. Variation of the dilution factor is employed to generate a dynamic range of concentrations for each RM and to evenly furnish the calibration lines to analyze certain matrices. A wide range of dilution factors were employed from 2–54 times dilution (with respect to the flux to sample ratios). Calibration lines for the major elements including: Si, Al, Ca, Fe, Mg, Na, Mn, and Ti show an extremely high level of linearity with all elements. R 2 values greater than 0.9990 were obtained for each analyzed element. The calibration application was validated by checking against a variety of geological RMs including petroleum and carbonate rich shale (SGR‐1), Muscovite rich marine shale (SBC‐1), metamorphic rock (SDC‐1), carbonatite (COQ‐1), and types of igneous rocks (GSP‐2, BCR‐2, AGV‐2, QLO‐1, and W‐2). Mixtures of Alumina and Silica (ARG‐1 and ARG‐2) and pure SiO 2 beads were also analyzed to further check the application. Rigorous statistical analysis on the RMs confirms the reliability of the calibration application for the employed matrices. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".