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Record W4406306084 · doi:10.1088/0026-1394/62/1a/08001

CCQM-K158 elements and inorganic arsenic in rice flour

2025· article· en· W4406306084 on OpenAlexaff
Yong‐Hyeon Yim, Kyoung‐Seok Lee, Kazumi Inagaki, Tomoko Ariga, Youngran Lim, Jong‐Wha Lee, Yanbei Zhu, Tomohiro Narukawa, Wai-hong Fung, Siu-kuen Tong, R.Y.C. Shin, Fransiska Dewi, Sim Lay Peng, Leung Ho Wah, Carlos Andrés España Sánchez, Márcia Silva da Rocha, Thiago de Oliveira Araújo, Osvaldo Reyes Acosta, Mabel Puelles, Radojko Jačimović, Marta Jagodic Hudobivnik, Ramiro Pérez Zambra, Romina Napoli, Heidi Goenaga‐Infante, Sarah Hill, John Entwisle, Christian Ward-Deitrich, Panayot Petrov, Silvia Mallia, Simon Lobsiger, Xiao Li, Qian Ma, Nattikarn Ornthai, Randa N. Yamani, Jeffrey Merrick, Angelique Botha, Patrícia Grinberg, Zuzana Gajdosechova, Kenny Nadeau, Olaf Rienitz, Axel Pramann, Volker Goerlitz, Anita Roethke

Bibliographic record

VenueMetrologia · 2025
Typearticle
Languageen
FieldChemistry
TopicHeavy Metals in Plants
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMutual recognitionIsotope dilutionCalibrationArsenicInductively coupled plasma mass spectrometryRice flourMathematicsMatrix (chemical analysis)ChemistryAnalytical Chemistry (journal)RadiochemistryEnvironmental chemistryMass spectrometryChromatographyStatistics

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.015
metaresearch head score (Gemma)0.012
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 score0.031
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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