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Construction and comparison of high temperature fixed points at NRC and CEM

2023· article· en· W4385351612 on OpenAlexaffabout
J. M. Mantilla, D. Woods, R. Emms, M. J. Martı́n, A. D. W. Todd

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCrucible (geodemography)TungstenEmissivityMaterials scienceMelting pointMetallurgyPhysicsChemistryComposite materialOptics

Abstract

fetched live from OpenAlex

Abstract This paper describes the collaborative project between National Research Council of Canada (NRC) and Centro Español de Metrología (CEM) for the construction and comparison of high temperature fixed points (HTFPs). A hybrid-type crucible that uses the piston method for filling has been jointly developed. A series of 12 high temperature fixed point blackbodies (HTFPBBs) have been constructed, including Cu, Ru-C, Ir-C, Re-C and WC-C covering the temperature range from 1084°C to 2750°C. All these cells were filled in NRC facilities by CEM and NRC staff. Two cells of each type of fixed point were constructed (except for the WC-C cells), using crucibles from two different suppliers in order to study the influence of the density and emissivity of the crucible in the HTFPs’ performance. Moreover, four WC-C cells were made using tungsten powder from two different suppliers, covering all the possible combinations of tungsten powder and crucibles acquired for this project. All cells, and additionally a Pt-C HTFPBB belonging to NRC, have been measured firstly at NRC and, afterwards, at CEM. Both laboratories have calculated the ITS-90 temperatures from their respective measurements and the results have been compared. Differences on the ITS-90 temperatures of the HTFPBBs measured at each lab are within uncertainties of the comparison. After the comparison, CEM kept one Cu cell, one Ru-C cell, one Re-C cell and two WC-C cells. The rest of the fixed points involved in the comparison were sent back to NRC.

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 score0.060
Threshold uncertainty score0.238

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 head0.026
GPT teacher head0.243
Teacher spread0.217 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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