MétaCan
Menu
Back to cohort
Record W3082362945 · doi:10.1088/0026-1394/57/1a/04002

Calibration of 1-D CMM artefacts: step gauges (EURAMET.L-K5.2016)

2020· article· en· W3082362945 on OpenAlexaff
Tim Coveney, Michael Matus, Shihua Wang, Ville Byman, Antti Lassila, Nasser Alqahtani, F Alqahtani, SUMNER DEAN, Felix Meli, Gian Bartolo Picotto, R. Bellotti, Osamu Satô, Rina Sharma, Girija Moona, Vinod Kumar, J. J. Rodriguez, E Prieto, İlker Meral, Okhan Ganioğlu, José Antonio Salgado, Adam Wójtowicz, Pavel Skalník, Vít Zelený, John R. Stoup, Gerard Kotte, Richard Koops, Edgar Arizmendi, Weinong Wang, Agneta Jakobsson, Alexandru Duta, Elena Dugheanu, Greg Reain, Gábor Szikszai

Bibliographic record

VenueMetrologia · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMutual recognitionMathematicsCalibrationStatisticsEquivalence (formal languages)MetrologyDiscrete mathematics

Abstract

fetched live from OpenAlex

The results of the inter-RMO key comparison EURAMET.L-K5.2016 on the calibration of a step gauge are reported. Twenty-two National Metrology Institutes from four different metrological regions all over the world participated in this comparison which lasted three years, from December 2015 to December 2018. Two artefacts were circulated so that the varying ranges of participants equipment could be accommodated. A 1020 mm ceramic monolithic step gauge remained stable throughout the comparison. A 610 mm steel step gauge changed length, possibly due to an impact while travelling between participants. The comparison of this artefact was divided into two groups, those before the damage and those after, with the reference value for each group derived from a linking participant who had demonstrated equivalence in the 1020 mm artefact circulation. For the 1020 mm comparison the inverse-variance weighted mean was taken as reference value. Of the twenty two participants, eleven successfully demonstrated the validity of the claimed measurement capability. Of the remaining 11, 3 submitted revised uncertainties after the initial circulation of results which were shown to be valid when compared to the reference value. A set of recommendations and actions were agreed with the remaining participants. 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 CCL, 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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.514
Threshold uncertainty score0.444

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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMetrologiaSame topicAdvanced X-ray and CT ImagingFrench-language works237,207