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Record W1995948329 · doi:10.1144/geochem2013-199

Analysis of powdered reference materials and known samples with a benchtop, field portable X-ray fluorescence (pXRF) spectrometer: evaluation of performance and potential applications for exploration lithogeochemistry

2014· article· en· W1995948329 on OpenAlexaff
Stephen J. Piercey, Mary C. Devine

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2014
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsX-ray fluorescenceSpectrometerFluorescenceMaterials scienceAnalytical Chemistry (journal)ChemistryOpticsPhysicsChromatography

Abstract

fetched live from OpenAlex

Powdered international reference materials and samples with previously obtained conventional geochemical data were analysed using a benchtop portable X-ray fluorescence (pXRF) spectrometer to test the abilities of pXRF in silicate rock lithogeochemistry. Results from international reference materials illustrate that pXRF can provide very precise data for many major, minor, and trace elements, generally with RSD values of <7.5 % and many <5 %, except at very low concentrations (i.e. approaching the limit of detection). Despite good precision, accuracy is highly variable and ranges from excellent to reasonable for many major and minor elements (±15–20 % relative difference, RD, for Al 2 O 3 , SiO 2 , K 2 O, CaO, Fe 2 O 3 , TiO 2 , and MnO±S), base metals (±20 % for Cu, Zn), the low field strength (LFSE) and high field strength elements (HFSE) (±15 % RD for Rb, Ba, Zr; ±20 % RD for Nb). Poor accuracy was obtained for MgO, P 2 O 5 , and the transition elements (V, Cr, Ni); Sr shows variable accuracy. Comparison of pXRF results to independent samples with data from conventional analyses illustrates very poor correlation for MgO, P 2 O 5 , V, Cr, and Ni, suggesting they have poor accuracy by pXRF. Aluminum (Al 2 O 3 ), SiO 2 , and Zn have r 2 values of c . 0.6–0.7 illustrating reasonable correlation, whereas most other elements (S, K 2 O, CaO, TiO 2 , MnO, Fe 2 O 3 , Co, Cu, Pb, Rb, Sr, Ba, Zr, Nb, U, As, and Mo) have very good to excellent correlation between pXRF data and conventional analysis (i.e. r 2 >0.80). In addition, many of the elements with r 2 >0.8 have slopes that are close to 1 or within 20 % of ideal, indicating that pXRF is replicating the results of conventional analyses and likely within ±20 % of what can be obtained by conventional methods. Down-hole profiles of pXRF data and element ratios replicate the geometry of the profiles from conventional analyses and illustrate the ability of the pXRF to discriminate rock type, alteration, and mineralization in unknown samples. Portable XRF can provide fit-for-purpose data that is useful in discriminating lithogeochemical variations related to lithology, alteration, and mineralization. However, pXRF should be considered a preliminary screening tool for sample selection and not a substitute for conventional lithogeochemical methods (e.g. XRF, fusion ICP-ES and ICP-MS), particularly when important economic decisions are to be made using such data (e.g. NI-43-101 resource calculations). Supplementary Material: Collated data for repeat analyses of reference materials in Mining Plus (Table 1) and Soil 3 Beam (Table 2) modes. Tables 3-9 contain plots comparing results from pXRF to accepted values for various reference materials. All tables are available at at www.geolsoc.org.uk/SUP18735

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)0.003
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.225
Teacher spread0.202 · 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

Citations58
Published2014
Admission routes1
Has abstractyes

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