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Record W2603180446 · doi:10.1088/0026-1394/54/1a/08013

Final report on CCQM-K125: elements in infant formula

2017· article· en· W2603180446 on OpenAlexaff
Jeffrey Merrick, David Saxby, E S Dutra, Rodrigo Caciano de Sena, Thiago de Oliveira Araújo, Marcelo Dominguez de Almeida, Lu Yang, Indu Gedara Pihillagawa, Zoltán Mester, Soraya Sandoval, Chao Wei, Marlene Castillo, Caroline Oster, P Fisicaro, Olaf Rienitz, Carola Pape, Ursula Schulz, Reinhard Jährling, Volker Görlitz, Eugenia Lampi, Elias Kakoulides, Della Wai-mei Sin, Yiu-chung Yip, Y T Tsoi, Yanbei Zhu, Tom Oduor Okumu, Yong‐Hyeon Yim, Sung Woo Heo, Myung Sub Han, Youngran Lim, M Arce Osuna, L Regalado, Christian Uribe, Mirella Buzoianu, Steluta Duta, L A Konopelko, A. I. Krylov, R.Y.C. Shin, Maré Linsky, Angelique Botha, Bertil Magnusson, Conny Haraldsson, Usana Thiengmanee, Hanen Klich, Süleyman Z. Can, F Gonca Coskun, Meryem Tunç, John Entwisle, J O'Reilly, Sarah Hill, Heidi Goenaga‐Infante, Michael R. Winchester, Savelas A. Rabb, Ron M Perez

Bibliographic record

VenueMetrologia · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsInductively coupled plasma mass spectrometryAnalytical Chemistry (journal)Microwave digestionInductively coupled plasmaChemistryMass spectrometryMutual recognitionAtomic absorption spectroscopyInductively coupled plasma atomic emission spectroscopyAtomic spectroscopyIsotope dilutionDetection limitChromatographySpectroscopyPlasmaPhysics

Abstract

fetched live from OpenAlex

CCQM-K125 was organized by the Inorganic Analysis Working Group (IAWG) of CCQM to assess and document the capabilities of the national metrology institutes (NMIs) or the designated institutes (DIs) to measure the mass fractions of trace elements (K, Cu and I) in infant formula. Government Laboratory, Hong Kong SAR (GLHK) acted as the coordinating laboratory. In CCQM-K125, 25 institutes submitted the results for potassium, 24 institutes submitted the results for copper and 8 institutes submitted the results for iodine. For examination of potassium and copper, most of the participants used microwave-assisted acid digestion methods for sample dissolution. A variety of instrumental techniques including inductively coupled plasma mass spectrometry (ICP-MS), isotope dilution inductively coupled plasma mass spectrometry (ID-ICP-MS), inductively coupled plasma optical emission spectrometry (ICP-OES), atomic absorption spectrometry (AAS), flame atomic emission spectrometry (FAES) and microwave plasma atomic emission spectroscopy (MP-AES) were employed by the participants for determination. For analysis of iodine, most of the participants used alkaline extraction methods for sample preparation. ICP-MS and ID-ICP-MS were used by the participants for the determination. Generally, the participants' results of CCQM-K125 were found consistent for all measurands according to their equivalence statements. Except with some extreme values, most of the participants obtained the values of d i / U ( d i ) within ± 1 for the measurands. Main text To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/ . The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.316
Teacher spread0.285 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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