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Record W4212953206 · doi:10.1088/0026-1394/59/1a/08004

Final report on CCQM-K167: carbon isotope delta measurements of vanillin

2022· article· en· W4212953206 on OpenAlex
Michelle M. G. Chartrand, Ian Chubchenko, Philip J. H. Dunn, Bruno Carius Garrido, Hai Lu, Fong‐Ha Liu, Nives Ogrinc, Adnan Şimşek, Maíra Fasciotti, Heidi Goenaga‐Infante, Sarah Hill, А В Колобова, Dmitry Malinovsky, Jeffrey Merrick, Eliane Cristina Pires do Rego, Doris Potočnik, Thays Vieira da Costa Monteiro, Wagner Wollinger, Xiao Li, Blaza Toman, Zoltán Mester, Juris Meija

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 · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMutual recognitionMathematicsEnvironmental scienceStatisticsData miningComputer science

Abstract

fetched live from OpenAlex

Main text The CCQM Isotope Ratio Working Group (IRWG) determined that an additional key comparison of carbon isotope delta measurements was required to capture the progress of this field. Vials containing 0.25 mg of vanillin were prepared at NRC, and evaluated for bottle-to-bottle homogeneity prior to distribution to the eight participating institutes. Participants were able to choose any suitable method and reference materials for carbon isotope delta measurements, and report a carbon isotope delta value and the associated uncertainty, and analysis details. To determine the key comparison reference value (KCRV) and its associated uncertainty, the NRC in collaboration with the NIST, developed a multivariate Bayesian random laboratory effects model, which also incorporates the uncertainty due to bottle-to-bottle homogeneity and any correlations between the reported results that arise due to the use of common reference materials. The KCRV for this study was determined to be -25.833 ‰ relative to the VPDB, with associated uncertainty of 0.028 ‰, and expanded uncertainty of 0.056 ‰ (k=2). All the results reported by the participants in this study were considered equivalent to the KCRV. 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 https://www.bipm.org/kcdb/ . 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.040
GPT teacher head0.259
Teacher spread0.219 · 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