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Record W3117876348 · doi:10.1088/0026-1394/58/1a/08006

CCQM-K143 comparison of copper calibration solutions prepared by NMIs/DIs

2020· article· en· W3117876348 on OpenAlex
John L. Molloy, Michael R. Winchester, Therese A. Butler, Antonio Possolo, Olaf Rienitz, Anita Roethke, Volker Goerlitz, Rodrigo Caciano de Sena, Marcelo Dominguez de Almeida, Lu Yang, Brad Methven, Kenny Nadeau, Patricia Romero Arancibia, Bing Wu, Tao Zhou, James Snell, Jochen Vogl, Maren Koenig, R.K. Kotnala, S. Swarupa Tripathy, Christine Elishian, Rosi Ketrin, Toshihiro Suzuki, Tom Oduor Okumu, Yong‐Hyeon Yim, Sung Woo Heo, Hyung Sik Min, Myung Sub Han, Youngran Lim, Judith Velina Lara Manzano, Francisco Segoviano Regalado, María del Rocio Arvizu Torres, Edith Valle Moya, Mirella Buzoianu, А. В. Собина, Veniamin M. Zyskin, E. P. Sobina, P. V. Migal, Maré Linsky, Süleyman Z. Can, Betül Arı, Heidi Goenaga‐Infante

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMetrologia · 2020
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNISTCalibrationCopperAnalytical Chemistry (journal)Calibration curveMathematicsChemistryMaterials scienceDetection limitChromatographyComputer scienceStatisticsMetallurgy

Abstract

fetched live from OpenAlex

Main text CCQM-K143 is a key comparison that assesses participants' ability to prepare single element calibration solutions. Preparing calibration solutions properly is the cornerstone of establishing a traceability link to the International System of Units (SI), and therefore should be tested in order to confirm the validity of CCQM comparisons of more complex materials. CCQM-K143 consisted of participants each preparing a single copper calibration solution at 10 g/kg copper mass fraction and shipping 10 bottled aliquots of that solution to the coordinating laboratory, the National Institute of Standards and Technology (NIST). The masses and mass fraction for the prepared solutions were documented with the submitted samples. The solutions prepared by all participants were measured at NIST by high performance inductively coupled plasma optical emission spectroscopy (HP-ICP-OES). The intensity measurements for copper were not mapped onto values of mass fraction via calibration. Instead, ratios were computed between the measurements for copper and simultaneous measurements for manganese, the internal standard, and all subsequent data reductions, including the computation of the KCRV and the degrees of equivalence, were based on these ratios. Other than for two participants whose measurement results appeared to suffer from calculation or preparation errors, all unilateral degrees of equivalence showed that the measured values did not differ significantly from the KCRV. These results were confirmed by a second set of ICP-OES measurements performed by the Physikalisch-Technische Bundesanstalt (PTB). CCQM-K143 showed that participants are capable of preparing calibration solutions starting from high purity, assayed copper metal. Similar steps are involved when preparing solutions for other elements, so it seems safe to infer that similar capabilities should prevail when preparing many different, single-element solutions. 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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.674

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)0.0010.000

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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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