Combined X‐ray diffraction and alpha particle X‐ray spectrometer analysis of geologic materials
Bibliographic record
Abstract
The shallow interrogation depth of the lightest elements (Na, Mg, Al, and Si) detected by the particle‐induced X‐ray emission branch of the Curiosity Rover's alpha particle X‐ray spectrometer suggests that the X‐rays of these elements very likely emerge from a single mineral grain. This reality violates the assumption of atomic homogeneity at the micron scale made in both existing spectrum‐reduction approaches for the alpha particle X‐ray spectrometer. Consequently, analytical results for these elements in igneous geochemical reference materials exhibit deviations from certified concentrations in a manner that can be related to the total alkali‐silica diagram. A computer code is introduced here to provide quantitative prediction of these deviations using the mineral abundances determined from X‐ray diffraction. The latter are converted to area coverage fractions to represent the sample surface, and a fundamental parameters computation predicts the elemental X‐ray yields from each mineral and sums these. In this process, the chemistry of each individual mineral has to be varied by an iterative simplex approach; X‐ray yields are computed and compared with the peak areas from the fit of the bulk sample. When the difference between mineral yields and peak areas for each element are minimized, the mineral formulae are set and elemental X‐ray yields provided. The ratio between the summed mineral X‐ray yields and the corresponding yields based on the homogeneity assumption may then be compared directly with the concentration deviations measured in our earlier work. For several rock types, good agreement is found, thereby consolidating our understanding of the effects of sample mineralogy on alpha particle X‐ray spectrometer results. Copyright © 2017 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".