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Record W2073315080 · doi:10.1088/0026-1394/40/1/311

A bilateral comparison of spectral responsivity measurements in the spectral range 250 nm to 1800 nm between the NIST (USA) and the NRC (Canada)

2003· article· en· W2073315080 on OpenAlexaffabout
Louis‐Philippe Boivin, Sally S. Bruce

Bibliographic record

VenueMetrologia · 2003
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNISTGermaniumMaterials scienceInternational Temperature Scale of 1990OpticsSemiconductor detectorDetectorSiliconCalibrationOptoelectronicsPhysicsComputer science

Abstract

fetched live from OpenAlex

This intercomparison of spectral responsivity between the National Institute of Standards and Technology (NIST) in the United States and the National Research Council (NRC) of Canada was carried out to test the level of agreement between the two laboratories for routine detector calibrations and also to support the uncertainties quoted by NIST and NRC in appendix C of the Mutual Recognition Arrangement (MRA) drawn up by the International Committee of Weights and Measures (CIPM). The comparison was carried out in two stages: 250 nm to 1100 nm using three silicon (Si) photodiodes, then 700 nm to 1800 nm using two germanium (Ge) and two indium gallium arsenide (InGaAs) detectors. This paper describes briefly the derivation of the spectral responsivity scales, measurement procedures and apparatus used for each laboratory. The results of the intercomparison are presented. In the spectral ranges from 250 nm to 1000 nm for silicon and 750 nm to 1700 nm for germanium and InGaAs, the agreement between the two laboratories is well within the combined uncertainties. In particular, in the 450 nm to 1000 nm spectral range, the agreement is better than ±0.2%. In the 900 nm to 1600 nm spectral range, the agreement is typically ±0.5%. Consequently, the results of this intercomparison are shown to support the uncertainties claimed by NIST and NRC in their routine calibrations, and in their appendix C entries of the 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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.054
GPT teacher head0.271
Teacher spread0.218 · 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 designObservational
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

Citations4
Published2003
Admission routes2
Has abstractyes

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