Portable X-ray fluorescence measurements on exploration drill-cores: comparing performance on unprepared cores and powders for ‘whole-rock’ analysis
Bibliographic record
Abstract
One geoscience application of portable XRF (pXRF) technology is acquiring ‘whole-rock’ analyses of unmineralized or weakly mineralized rock cores for major oxides and trace elements, to fill the gaps between traditional laboratory analyses and/or obtain geochemical data more quickly. But the question of whether the samples actually need to be crushed and pulverized before analysis to produce useful results has not been extensively studied. In this paper pXRF data quality is compared on unprepared rock cores and on powders in three ways: instrumental precision (relative standard deviation [RSD] of a series of measurements on the same spot), sample precision (for unprepared samples, RSD of a series of measurements on different spots on the core), and accuracy (average pXRF value versus laboratory geochemistry). Two Olympus Innov-X Delta Premium pXRF devices were tested on 27 core samples of dense, non-mineralized, fine- to medium-grained, Precambrian volcanic and intrusive rocks from Canada. In general, sample preparation does not improve instrumental precision or accuracy. The significant advantage of powders is to avoid mineralogical heterogeneity. However, sample precision for in situ data is improved by averaging multiple measurements of different points on the sample: a significant gain is obtained between three and seven measurements. The sample precisions at 25 points – which is about the most measurements one can make during the same amount of time used for powdering a rock core sample – are better than the instrumental precision on powders for most elements. For high spatial resolution down-hole element profiles on entire drill-holes, in situ pXRF measurements with smoothing (e.g. 3–5 point moving averages) provide fit-for-purpose data; the alternative of turning the entire drill-core into powder is not realistic.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".