Hard X-Ray Spectro Microprobe Analysis of Inhomogeneous Solids: A Case Study. Element Distribution and Speciation in Selected Iron Meteorites
Bibliographic record
Abstract
The hard X‐ray microprobe provides an effective methodology for the non‐destructive analysis of inhomogeneous materials. Application of X‐ray absorption/fluroescence spectroscopy techniques (XANES and EXAFS) permits the speciation of the elements and yields information about the local structural environment. Microfocussed, monochromatic, tunable X‐rays allows examination of small areas of micrometer dimensions with spectroscopic procedures. Typically the materials which are presented are thick and cannot be altered for the experiment. This condition introduces difficulties which may compromise the results. Herein we discuss those difficulties and show that the system can yield reliable results in spite of the compromises. Some results are presented on the two iron meteorites we have examined. These specimens are representative of highly inhomogeneous materials and illustrate the difficulties encountered with compositional variations which may occur at sub‐millimeter dimensions and also illustrate the difficulties presented by the need to analyze components present at ppm concentration levels in a concentrated matrix. In these particular samples the major constituent is Fe which ranges from 90% to 70%, balanced by Ni which ranges from 10% to 30%. The critical diagnostic trace elements Ga and Ge which must also be analyzed are present at the 80 and 340 ppm level respectively. These diagnostic elements have been shown by EXAFS to be substitutionally placed in the matrix of the major element species in these meteorite samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".