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Record W2054636759 · doi:10.1121/1.4743458

International calibration comparisons: Who benefits?

2000· article· en· W2054636759 on OpenAlexaff
George S. K. Wong

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2000
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBeneficiaryCalibrationInternational comparisonsTraceabilityMetrologyCertificationTariffMutual recognitionComputer scienceBusinessAccountingEconomicsFinanceInternational tradeStatisticsMathematicsEconomic growth

Abstract

fetched live from OpenAlex

The mutual acceptance of acoustical calibrations and measurements between industrial countries is essential for international trade and the removal of no-tariff trade barriers. For example, the sound level or sound power emitted by a machine measured at the country of manufacture with certified instruments and methods in accordance with international standards, should be acceptable by the importing country without the requirement to duplicate the measurements. To achieve this mutual recognition, it is necessary for the exporting country to have proven capabilities via international comparisons and an unbroken chain of traceability from their national metrology institute to the machine shop level. Under the umbrella of the Bureau International does Poids et Mesures (BIPM), the Consultative Committee on Acoustics, Ultrasound and Vibration (CCAUV) has arranged international calibration comparisons, involving over 15 countries. The above comparisons require a lot of effort from each participating country. One may ask the question: Who is the beneficiary of international comparisons? The detailed answer is rather complex. In general, the results of International Calibration Comparisons provide confidence in the measurement capabilities of the participants. In the long term, the consumer is the ultimate beneficiary.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.799
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.130
GPT teacher head0.369
Teacher spread0.239 · 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.

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

Citations0
Published2000
Admission routes1
Has abstractyes

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