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Record W2784211962 · doi:10.1088/1681-7575/aaa677

Establishing comparability and compatibility in the purity assessment of high purity zinc as demonstrated by the CCQM-P149 intercomparison

2018· article· en· W2784211962 on OpenAlexaff
Jochen Vogl, Heinrich Kipphardt, Silke Richter, Wolfram Bremser, María del Rocio Arvizu Torres, Judith Velina Lara Manzano, Mirella Buzoianu, Sarah Hill, Panayot Petrov, Heidi Goenaga‐Infante, Mike Sargent, Paola Fisicaro, Guillaume Labarraque, Tao Zhou, Gregory C. Turk, Michael R. Winchester, Tsutomu Miura, Brad Methven, Ralph E. Sturgeon, Reinhard Jährling, Olaf Rienitz, Michal Máriássy, Zuzana Hanková, E. P. Sobina, A. I. Krylov, Y A Kustikov, Vadim Vladimirovich Smirnov

Bibliographic record

VenueMetrologia · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsComparabilityCompatibility (geochemistry)Environmental scienceMaterials scienceMathematics

Abstract

fetched live from OpenAlex

Abstract For the first time, an international comparison was conducted on the determination of the purity of a high purity element. Participants were free to choose any analytical approach appropriate for their institute’s applications and services. The material tested was a high purity zinc, which had earlier been assessed for homogeneity and previously used in CCQM-K72 for the determination of six defined metallic impurities. Either a direct metal assay of the Zn mass fraction was undertaken by EDTA titrimetry, or an indirect approach was used wherein all impurities, or at least the major ones, were determined and their sum subtracted from ideal purity of 100%, or 1 kg kg −1 . Impurity assessment techniques included glow discharge mass spectrometry, inductively coupled plasma mass spectrometry and carrier gas hot extraction/combustion analysis. Up to 91 elemental impurities covering metals, non-metals and semi-metals/metalloids were quantified. Due to the lack of internal experience or experimental capabilities, some participants contracted external laboratories for specific analytical tasks, mainly for the analysis of non-metals. The reported purity, expressed as zinc mass fraction in the high purity zinc material, showed excellent agreement for all participants, with a relative standard deviation of 0.011%. The calculated reference value, w (Zn) = 0.999 873 kg kg −1 , was assigned an asymmetric combined uncertainty of +0.000 025 kg kg −1 and −0.000 028 kg kg −1 . Comparability amongst participating metrology institutes is thus demonstrated for the purity determination of high purity metals which have no particular difficulties with their decomposition/dissolution process when solution-based analytical methods are used, or which do not have specific difficulties when direct analysis approaches are used. Nevertheless, further development is required in terms of uncertainty assessment, quantification of non-metals and the determination of purity of less pure elements and/or for those elements suffering difficulties with the decomposition process.

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)0.140
metaresearch head score (Gemma)0.134
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.740

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1400.134
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0040.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.211
GPT teacher head0.455
Teacher spread0.244 · 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

Citations28
Published2018
Admission routes1
Has abstractyes

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