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Record W2029663504 · doi:10.1121/1.4779455

Microphone interlaboratory comparison in the Americas

2002· article· en· W2029663504 on OpenAlexaffabout
George S. K. Wong, Lixue Wu

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNISTMetrologyMicrophoneNational standardCalibrationGeographyLibrary scienceTelecommunicationsEngineeringStatisticsMathematicsComputer scienceSpeech recognition

Abstract

fetched live from OpenAlex

The final results of a Sistema Interamericano de Metrologia (SIM) interlaboratory comparison on microphone calibration are presented. Initially the comparison involved NORAMET countries: USA, Canada, and Mexico. Later, the comparison was extended to include Argentina and Brazil, resulting in a SIM AUV.A-K1 microphone interlaboratory comparison. The National Metrology Institutes (NMIs) of the five American countries that participated were the Institute for National Measurement Standards (INMS—Canada), National Institute of Standards and Technology (NIST—USA), Centro Nacional de Metrología (CENAM—Mexico), Instituto Nacional de Metrologia, Normalização e Qualidade Industrial (INMETRO—Brazil) and Unidad Técnica Acústica, (INTI—Argentina). INMS, Canada was the pilot laboratory that provided the data for the final report. The maximum rms deviation for the two LS1P laboratory standard microphones measured by the above participants is 0.037 dB that may be considered as the key comparison reference value.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2002
Admission routes2
Has abstractyes

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