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Record W3116387215 · doi:10.1144/geochem2020-075

Use of field-portable XRF in exploration of PGE-enriched zones in the Pilanesberg PGE deposit, Bushveld Complex, South Africa

2020· article· en· W3116387215 on OpenAlexaff
Alireza K. Somarin, Ingo Steinhage

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2020
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsBrandon University
Fundersnot available
KeywordsGeologyGeochemistryField (mathematics)Mining engineeringMineralogy

Abstract

fetched live from OpenAlex

The field-portable X-ray fluorescence (FPXRF) spectrometer has evolved significantly in the last decade and has become one of the most innovative tools for field geologists. Portability and ease of use of FPXRF systems have opened up new and unique applications for even novice technicians. Application of FPXRF in precious metals exploration and mining appears to be challenging due to their low concentrations (lower than detection limit by FPXRF) in nature and even in most ore deposits. This case study shows the success of FPXRF in identifying anomalous zones of platinum group elements (PGE) and Au (target elements) using pathfinder elements in the Pilanesberg PGE deposit, Bushveld Complex, South Africa. Sixty-three core samples were analysed using both FPXRF and laboratory methods. In these samples, Pt <8 ppm, Pd <5 ppm and Au <1 ppm, which were not detected by FPXRF; however, Ni and Cu are up to 6540 and 3560 ppm, respectively, which were easily detected by the same method. These elements show positive correlation with the precious metals indicating that they can be used as pathfinders. Both direct shot analyses of core samples and their pulverized specimen assays show correlation with lab assay data suggesting that both methods can be used in the field; however, the accuracy of direct shot data is lower due to the heterogeneity of samples.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.624
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.220
Teacher spread0.142 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2020
Admission routes1
Has abstractyes

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