Improving lithological discrimination in exploration drill-cores using portable X-ray fluorescence measurements: (1) testing three Olympus Innov-X analysers on unprepared cores
Bibliographic record
Abstract
Portable X-ray fluorescence (pXRF) analysers are increasingly popular tools for geoscientific applications, including mineral exploration. One promising application, illustrated in the companion paper, is to obtain high-spatial resolution down-hole geochemical profiles using pXRF on unprepared exploration drill-cores. However, the precision and accuracy of pXRF analysers on such samples is not well studied. We have tested three Olympus Innov-X analysers, both on a sediment standard (NIST 2702, ‘Inorganics in Marine Sediment’) and in-situ on unmineralized rock cores from volcanic and intrusive, mafic to felsic lithologies. We conclude that pXRF is quite precise for a number of elements, but not very accurate using factory calibrations. For example, the 1σ precision of one Delta Premium analyser tested on a basaltic core, in mining plus mode, with a 60 s integration time, is better than 5 % for Al, Ca, Fe, K, Mn, S, Si, Ti, Zn and Zr. The same analyser, tested on a range of volcanic and intrusive core samples, yielded the following average systematic errors: Al -23 %, Ca -4 %, Fe +1 %, K -9 %, Mg -17 %, Mn -15 %, P +218 %, Si +4 %, Ti -23 %, Cu +220 %, Zn +151 %, and Zr +17 %. These systematic errors can largely be removed by the application of correction factors, which are unique to each analyser and each project. Without such corrections, the three analysers tested, including two ‘identical’ Delta Premium models, yield different results on the same sample. Another important finding is that within 20 cm long core samples, the effect of mineralogical heterogeneity on in-situ pXRF data is much larger than that of the instrument precision. Finally, with the Delta analysers, both the ‘mining plus’ and the ‘soil’ modes are needed to determine as many elements as possible with the best data quality possible.
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 | 0.002 | 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 | 0.000 |
| 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".