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Record W2043305099 · doi:10.1144/geochem2012-177

Applicability of handheld X-Ray fluorescence spectrometry in the exploration and development of carbonatite-related niobium deposits: a case study of the Aley Carbonatite, British Columbia, Canada

2014· article· en· W2043305099 on OpenAlexaffabout
George J. Simandl, S J Paradis, Rebecca S. Stone, Robert Fajber, Ryan Kressall, K. T. V. Grattan, Jeremy Crozier, L.J. Simandl

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2014
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsDalhousie UniversityUniversity of VictoriaGeological Survey of Canada
Fundersnot available
KeywordsCarbonatiteNiobiumX-ray fluorescenceGeologyGeochemistryFluorescenceMetallurgyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

This study evaluates the suitability of portable (handheld) X-Ray fluorescence spectrometry (pXRF) in the exploration for Aley-type ‘hard-rock’ (primary) carbonatite-hosted Nb deposits. The assessment consists of comparisons between: (1) results of pXRF analyses on selected pulp samples and results of analyses of the same pulps using traditional laboratory methods; (2) results of averaged, multiple pXRF spot field analyses performed directly on 10 to 15 cm long pieces of core (before pulverization) compared with those of traditional laboratory analyses of the same pieces of core after pulverization; and (3) results of a manual core scanning method compared with the results of conventional analytical methods of the pulps of the corresponding scanned sections. A strong correlation exists between pXRF measurements on pulps and laboratory methods for most specialty metals, such as Nb (r 2 = 0.99), La (r 2 = 0.97), Ce (r 2 = 0.67), Y (r 2 = 0.93), and P (r 2 = 0.89); however, the values of r 2 for Pr and Nd are 0.19 and 0.38, respectively. As expected, textural heterogeneities within sample intervals reduced the quality of pXRF results when multiple spot readings were taken directly on the core. Nevertheless, the data can still be used to identify carbonatite-related Nb (± other specialty metal mineralization) and delimitate potentially economically significant zones within it. The core scanning reduced the degree of variation associated with spot analyses. Scanning is useful during the early exploration stages, but provides data limited by the inability of the operator to maintain constant scanning speed. The scanning results correlate with laboratory methods for Nb (r 2 = 0.88), Th (r 2 = 0.80), Fe (r 2 = 0.84), Sr (r 2 = 0.74), Ba (r 2 = 0.73), Y (r 2 = 0.59), and Zn (r 2 = 0.75). The values of r 2 for La, Ce, Pr, and Nd were only 0.31, 0.26, 0.01 and 0.03, respectively, suggesting that concentrations of these elements were too low, and/or that the light rare earth elements (LREEs) were present not only in the crystal structure of fersmite, pyrochlore and apatite, but also in minor or accessory minerals such as REE-bearing fluorocarbonates or zircon erratically distributed throughout the core. Portable XRF is a robust tool facilitating exploration-related decision-making in the field, assuming that elements of interest such as Nb are present in concentrations within the analytical range of the instrument. The pXRF core scanning reduces the need for sample preparation (no pulps) and can be done directly on the drill-site, but the precision and accuracy of the data are reduced relative to laboratory and pXRF pulp analyses. The multiple spot analyses (no pulps) approach is good for instant verification of unknown, potentially ore-bearing minerals and for analysing discrete homogeneous features, layers, veins, etc; however, under normal circumstances this method is inferior to pulp analyses in precision and accuracy, and to scanning for determining average grade of core intervals.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.177
Teacher spread0.168 · 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 designObservational
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

Citations20
Published2014
Admission routes2
Has abstractyes

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