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Record W2915218073 · doi:10.1088/0026-1394/55/1a/08003

Final report of the SIM.QM-S8 supplementary comparison, trace metals in drinking water

2017· article· en· W2915218073 on OpenAlexaffabout
Yang Lü, Kenny Nadeau, Indu Gedara Pihillagawa, Juris Meija, Zoltán Mester, Romina Napoli, Ramiro Pérez Zambra, Elizabeth Ferreira, Diego A. Ahumada, Johanna Paola Abella Gamba

Bibliographic record

VenueMetrologia · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMutual recognitionTRACE (psycholinguistics)MetrologyContext (archaeology)Systematic errorStatisticsMathematicsEnvironmental scienceComputer scienceLibrary scienceOperations researchGeographyArchaeologyBusiness

Abstract

fetched live from OpenAlex

After completing a supplementary comparison SIM.QM-S7, the National Metrology Institute of Colombia (NMIC) requested to National Research Council of Canada (NRC) a subsequent bilateral comparison, because INMC considered that its results in SIM.QM-S7 unrepresentative of its standards. In this context, NRC agreed to coordinate this bilateral comparison with the aim of demonstrating the measurement capabilities of trace elements in fresh water. Participants included NMIC and LATU. No measurement method was prescribed by the coordinating laboratory. Therefore, NMIs used measurement methods of their choice. However, the majority of NMIs/DIs used ICP-MS. This SIM.QM-S8 Supplementary Comparison provides NMIs with the needed evidence for CMC claims for trace elements in fresh waters and similar matrices. 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)0.001
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 score0.231
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.311
Teacher spread0.255 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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