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Record W2914143886 · doi:10.1088/0026-1394/56/1a/08009

Final report on APMP.QM-S10: elements in food supplement

2019· article· en· W2914143886 on OpenAlexaff
N Hatamleh, Mabel Puelles, Hernán Ezequiel Lozano, Md Ashfaque Hossain Khan, Lu Yang, I G Pihillagawa, Kari C. Nadeau, Zoltán Mester, Changshuai Wei, X Li, D W M Sin, W H Fung, Yeuk‐Ki Tsoi, Luigi Bergamaschi, Tom Oduor Okumu, Carolina Vaca Uribe, Alexander A. Stakheev, R.Y.C. Shin, Radojko Jačimović, Usana Thiengmanee, H Klich, J Raouf, N Chaabene, Sonia Driss Chaieb, Hien Thi Thu Ngo

Bibliographic record

VenueMetrologia · 2019
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsInductively coupled plasma mass spectrometryChemistryNeutron activation analysisIsotope dilutionManganeseMagnesiumZincAtomic absorption spectroscopyMass spectrometryMicrowave digestionDilutionAnalytical Chemistry (journal)RadiochemistryEnvironmental chemistryDetection limitChromatographyPhysics

Abstract

fetched live from OpenAlex

APMP.QM-S10 was coordinated by Government Laboratory, Hong Kong SAR (GLHK) under the auspices of the Inorganic Analysis Working Group (IAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM). Fourteen national metrology institutes (NMIs) or designated institutes (DIs) registered for the examination of zinc, manganese, calcium and magnesium. All institutes submitted the results for zinc and manganese, and twelve institutes submitted the results for calcium and magnesium. The supplementary comparison was designed to enable participating institutes to demonstrate their measurement capabilities in measuring the mass fractions of the analytes at mg/kg levels in a test sample of food supplement by various analytical techniques. For examination of zinc, manganese, calcium and magnesium, most of the participants used microwave-assisted acid digestion methods for sample dissolution. A variety of instrumental techniques including inductively coupled plasmas mass spectrometry (ICP-MS), isotope dilution inductively coupled plasmas mass spectrometry (ID-ICP-MS), inductively coupled plasmas optical emission spectrometry (ICP-OES), flame atomic absorption spectrometry (FAAS), and instrumental neutron action analysis (INAA) were employed by the participants for determination. For this supplementary comparison, inorganic core capabilities have been demonstrated by the concerned participants with respect to methods including ICP-MS (without isotope dilution), ID-ICP-MS, ICP-OES, FAAS and INAA on the determination of elements (zinc, manganese, calcium and magnesium) in a food matrix of food supplement. Generally, the participants' results of APMP.QM-S10 were found to be 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 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)0.009
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.101
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1010.054

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 head0.045
GPT teacher head0.314
Teacher spread0.269 · 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".

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Citations3
Published2019
Admission routes1
Has abstractyes

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