THE APPLICATION OF PORTABLE X-RAY DIFFRACTION TO QUANTITATIVE MINERALOGICAL ANALYSIS OF HYDROTHERMAL SYSTEMS
Bibliographic record
Abstract
X-ray powder diffraction (XRD) is a well-established tool in the study of hydrothermal systems, as it allows for the identification and quantification of mineral assemblages. Through the quantification of alteration mineral assemblages geologists can characterize the geometry of a deposit or geothermal system and draw inferences regarding the fluid evolution and environmental conditions of deposition ( e.g. , pH, T). Traditionally, XRD devices have largely been restricted to laboratories; however, advances in XRD sample holders and X-ray sources have allowed for the development of portable XRD (pXRD) devices. This paper assesses the validity of the Olympus Terra pXRD instrument for qualitative and quantitative studies of hydrothermal systems through comparisons with data from laboratory XRD (Empyrean II diffractometer) and XRF techniques for both synthetic mixtures of natural minerals and a variety of samples from the Kulumadau epithermal gold deposit, Woodlark Island, Papua New Guinea. Diffractograms of synthetic mineral mixtures with known concentrations of quartz, kaolinite, muscovite, albite, and pyrite were analyzed quantitatively using the Rietveld-based Siroquant technique and showed good overall agreement for both devices, with the exception of muscovite, which encountered accuracy issues at concentrations R 2 values of >0.92 for all major mineral phases ( i.e. , >5%). With the exceptions of pyrite and magnetite, minor to trace minerals ( i.e. ,
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".