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Record W2166574104 · doi:10.1144/geochem2013-241

Evaluation of portable X-ray fluorescence (pXRF) in exploration and mining: Phase 1, control reference materials

2014· article· en· W2166574104 on OpenAlexaboutno aff
G.E.M. Hall, Graeme Bonham-Carter, Angelina Buchar

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePhase (matter)FluorescenceGeologyPhysicsOptics

Abstract

fetched live from OpenAlex

This paper describes a project sponsored by the Canadian Mining Industry Research Organisation (CAMIRO) to evaluate the strengths and weaknesses of portable XRF for use in mineral exploration and mining and to develop best-practice protocols in the analysis of rocks, soils, sediments and drill-core. Phase I focussed on the analysis of pulp control reference materials (CRMs) to determine the figures of merit, principally accuracy and precision, of the technique before introducing the confounding parameters associated with in-situ analysis such as heterogeneity, particle size and moisture. Five instruments (three handheld and two portable benchtop) from three manufacturers were used to carry out replicate analyses (n = 10) of a diverse suite of 41 CRMs, from barren granites, through soils and sediments, to ores. Standard factory calibration, in mining and soil modes, was used. The performance of the instruments was evaluated using x-y plots of results versus established element concentrations for the CRMs and the values of goodness of fit (r 2 ), slope and intercept documented for both full and restricted concentration ranges. For many elements, the performance across the instruments varied markedly, as did the ability to correct for spectral interferences. Numerous interferences were encountered, particularly from the rare-earth elements (REEs) on transition elements, but also for well-known interference pairs such as Pb on As, Zn on Au, U on Mo, and Th on Bi. In general, major elements with the exception of the light element Mg were well determined, as were Mn and Ti. Sensitivity was inadequate for Cl and P; however, S could be measured with acceptable precision to c . 0.05% S. Performance for the trace elements was categorized as follows: very good for As, Cu, Nb, Pb, Rb, Sr, and Y (r 2 >0.9 for more than 1 instrument); good for Ba, Mo, Sn, Zn and Zr (r 2 >0.9 for 1 instrument); moderate for Cr, Sb, Se, Th and U (r 2 = 0.6–0.8); poor for Ag, Cd, Co, Ni and V; and very poor for Au, Bi, Cs, Hf, Hg, Pd, Sc, Ta, Te and W. Of the REEs determined (La, Ce, Nd, Sm, using the standard calibration by the manufacturer, i.e. not REE-specific), only La showed adequate sensitivity and precision (3–5% RSD), however, only at concentrations approaching c . 1000 ppm (50–100% RSD at La <100 ppm). Slopes of the best-fit lines, where r 2 ≥0.6, ranged from 0.5 to 5.0, indicating that calibration is required by the user for both the soil and mining modes. The precision, shown by 10 replicate readings, was excellent and usually better than 10% RSD except where close to detection limit or where major interferences were present. The beam time study showed that, in most situations, 60 s was a good compromise between productivity and precision but also highlighted cases of significant drift and a lack of improvement in precision with longer beam time. A study of the thin-film sample cover used for cups demonstrated that 4.0-µm Prolene® is superior to the same thickness of Mylar® in both transmittance (especially for Mg, Al, Si) and contamination properties.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.027
GPT teacher head0.266
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations124
Published2014
Admission routes1
Has abstractyes

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