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Record W4361854780 · doi:10.1088/0026-1394/60/1a/03001

ITS-90 SPRT calibration from the Ar TP to the Zn FP

2023· article· en· W4361854780 on OpenAlex
Tobias Herman, Michal Chojnacky, Ken Hill, Steffen Rudtsch, Inseok Yang, P. P. M. Steur, R Dematteis, Giuseppina Lopardo, F. Sparasci, Catherine Martin, L. Risegari, J. V. Widiatmo, Tohru Nakano, Ikuhiko Saito, Klaus N. Quelhas, Patricia Giorgio, Jianping Sun, Jintao Zhang, Jonathan Pearce, J. P. Gray

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 · 2023
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNISTMutual recognitionCalibrationStandard uncertaintyInternational Temperature Scale of 1990MathematicsEnvironmental scienceComputer scienceStatisticsMeasurement uncertainty

Abstract

fetched live from OpenAlex

Main text This is a report to the Consultative Committee for Thermometry (CCT) on the key comparison 9 of Standard Platinum Resistance Thermometer (SPRT) calibration on the International Temperature Scale of 1990 (ITS-90) [1] from 83.8058 K (the Ar triple point) to 692.677 K (the Zn freezing point). The comparison followed a collapsed star protocol, with each of the fourteen participating laboratories shipping two standard platinum resistance thermometers (SPRTs) as transfer standards to the pilot laboratory (National Institute of Standards and Technology, NIST) for a direct comparison of SPRT calibration at the National Metrology Institute (NMI) of origin to calibration at NIST. Measurements were taken at the participating laboratories before and after measurement at NIST to assess the effect of transportation on the transfer standards. The pooled results from all laboratories were combined to calculate a key comparison reference value (KCRV). This report includes the calculation of, and comparisons to, the KCRV as well as bilateral comparisons between labs. In addition, there is space devoted to analysis of the shifts in SPRT values following travel. 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 CCT, 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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.478

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 head0.038
GPT teacher head0.240
Teacher spread0.201 · 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