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Record W4294607960 · doi:10.1088/1681-7575/ac8d0f

No country for old borosilicate triple-point-of-water cells

2022· article· en· W4294607960 on OpenAlexaff
Sergey Dedyulin, Andrea Peruzzi

Bibliographic record

VenueMetrologia · 2022
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBorosilicate glassInternational Temperature Scale of 1990Inductively coupled plasma mass spectrometryImpurityMaterials scienceCalibrationChemistryMass spectrometryMetallurgyMathematicsChromatographyStatistics

Abstract

fetched live from OpenAlex

Abstract Temperature calibration labs all around the world typically maintain a set of several triple-point-of-water (TPW) cells for regular measurements using the International Temperature Scale of 1990 (ITS-90). This set often includes more-than-10-year-old TPW cells made from a borosilicate glass. In this paper we summarize the problems with old borosilicate TPW cells, using the available literature data, the results of CCT.K7-2021 key comparison and NRC TPW measurements, and highlight the solution to these issues—vitreous-silica TPW cells. The latter have an exceptional long-term stability, as we demonstrate by using: (a) comparative measurements of the same-age, same-manufacturer vitreous-silica and borosilicate TPW cells at NRC in a period from 2007 to 2021, and (b) inductively coupled plasma-mass spectrometry (ICP-MS) analysis of impurities present in the 18 year-old vitreous-silica outlier, cell Q325. Remarkably, not only all NRC vitreous-silica TPW cells remained stable over a 15 years time period, unlike their borosilicate counterparts, but the amount of impurities in the ‘coldest’ cell Q325 (−31 μK from the intercomparison reference value) is comparable to that in the newly purchased vitreous-silica cells. We argue that the most accurate TPW measurements, such as for defining the national TPW references and for the international key comparisons, should rely exclusively on vitreous-silica TPW cells.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.605

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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