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Record W4389229450 · doi:10.3397/in_2023_0040

Towards a revised international standard for personal sound exposure meters

2023· article· en· W4389229450 on OpenAlexaff
Peter Hanes

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsInternational standardSound level meterSound (geography)Noise (video)NormativeTest (biology)Sound exposureComputer scienceMeasure (data warehouse)CommissionEngineeringTelecommunicationsBusinessDatabaseNoise levelAcoustics

Abstract

fetched live from OpenAlex

Personal sound exposure meters and personal noise dosimeters are designed to be worn on a person and to measure sound exposure or sound exposure as a percentage of a predetermined criterion. Different jurisdictions employ different definitions of these quantities and different criteria for exposure. Existing normative standards for these instruments therefore differ in their specifications and are outdated due to changes in markets and technologies since their publication. The International Electrotechnical Commission is revising the applicable international standard (IEC 61252) to modernise and harmonise requirements for the instruments. The technical aims of the revision are to provide realistic specifications, methods for testing all relevant characteristics of a model of personal sound exposure meter, and methods for periodic testing of individual instruments. Specifications need to reflect the actual practice of measurements of noise exposure worldwide and test methods need to be consistent, realistic, and affordable. The needs of various users have been surveyed and used to prepare substantial technical changes to the specifications in the document. Experience with the equivalent international standard for sound level meters (IEC 61672) has been reviewed to draft new test methods that are appropriate for personal sound exposure meters.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.962

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 head0.027
GPT teacher head0.274
Teacher spread0.247 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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