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Trace Element Data for Gold, Iridium and Silver in Seventy Geochemical Reference Materials

2008· article· en· W2002943133 on OpenAlexafffund
Marc Constantin

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2008
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique Montréal
KeywordsIridiumPlatinum groupCertified reference materialsIsotope dilutionTrace elementAnalytical Chemistry (journal)RepeatabilityChemistryNeutron activation analysisHomogeneity (statistics)PlatinumDetection limitMineralogyRadiochemistryEnvironmental chemistryMass spectrometryChromatography

Abstract

fetched live from OpenAlex

New concentrations for Au, Ir and Ag obtained by instrumental neutron activation analysis are presented for seventy geochemical reference materials. Results in agreement with literature values for Au and Ir down to concentrations of a few ng g −1 were obtained. For Au and Ir concentrations above 10 ng g −1 , the repeatability of replicate analyses of reference materials was mostly better than 10%. For concentrations between 1 and 10 ng g −1 the RSD for Ir was 10–30%, whereas for Au it was higher and more variable (20–50%). In addition, concentrations for Cd and Hg are presented for some of the same reference materials. The high RSD at relatively high concentrations seen in gold for some RMs (e.g., WMG‐1, WMS‐1) did not exist for Ir and suggests homogeneity for this platinum‐group element at the sub‐sample size used in this study. For the following eight RMs, mostly ultramafic rocks (CHR‐Pt+, OREAS‐13P, OREAS‐14P, PCC‐1, UMT‐1, WMG‐1, WMS‐1, WPR‐1), Ir measurements agreed within ± 10% of mostly certified or recommended concentrations, which ranged from 2 ng g −1 to 6 μg g −1 . For the reference material UB‐N, iridium concentration compared favourably to published results obtained by isotope dilution ICP‐MS methods and a previously unrecognised heterogeneity is inferred for Au, Hg and Sb, but not for the other measured elements.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.153
GPT teacher head0.387
Teacher spread0.234 · 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 designNot applicable
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

Citations21
Published2008
Admission routes2
Has abstractyes

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