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Record W2089586606 · doi:10.1121/1.4785356

Hearing protector labeling for active noise reduction devices

2004· article· en· W2089586606 on OpenAlexaff
William J. Murphy, John R. Franks, Alberto Behar

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2004
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrophoneAcousticsTest fixtureBone conductionAttenuationHearing protectionNoise reductionNoise (video)Computer scienceEar canalMaterials scienceHearing lossAudiologyPhysicsOpticsMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

The US EPA regulation 40 CFR part 211b does not specify how to label hearing protectors that use active noise reduction (ANR). Real-ear attenuation at threshold (REAT) measurements are appropriate for testing the passive noise reduction performance of the earmuff. The contribution of ANR to the overall attenuation must be measured either with an acoustic test fixture (ATF) or using the microphone in real ear (MIRE) technique. ATFs must adequately mimic the coupling of the protector with either the skin of the ear canal or the side of the head, as well as provide sufficient attenuation to eliminate effects of bone conduction. The MIRE technique uses a miniature microphone positioned in the ear canal either at the entrance or at the tympanic membrane, and can be used to measure the insertion loss between the occluded and unoccluded conditions for both the passive and active modes of the protector. ATF and MIRE measurements made for several protectors demonstrate effective performance below 1000 Hz. This paper will examine methods for combining ATF and MIRE measurements with REAT to develop effective and informative rating for the ANR class of hearing protectors. [Portions of work supported by the U.S. EPA IA 75090527.]

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.368
Teacher spread0.330 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

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