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Record W3159326516 · doi:10.47176/mjiri.35.58

Acoustical role of ear canal in exposure to the typical occupational noise levels

2021· article· en· W3159326516 on OpenAlexaff
Hadi Asady, Adrián Fuente, Siamak Pourabdian, Farhad Forouharmajd, Ismail Shokrolahi

Bibliographic record

VenueMedical Journal of the Islamic Republic of Iran · 2021
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversité de Montréal
FundersIsfahan University of Medical Sciences
KeywordsSound pressureSound level meterAudiologyEar canalAcousticsPink noiseSound exposureNoise exposureMedicineStimulus (psychology)Noise (video)Noise levelSound (geography)PsychologyPhysicsHearing loss

Abstract

fetched live from OpenAlex

↑What is "already known" in this topic: Different ways are used in our ears to the best possible impedance matching achieved, and somehow the impedance mismatch that is released.These ways are the greater area of the eardrum to the oval window, and amplification actions of the middle ear ossicle, the ear canal, shoulder, head, and pinna. →What this article adds:Based on our knowledge, these amplification mechanisms have been neglected in the occupational health-related noise measurements and the hearing protection programs; thus, in this study, the effects of the external auditory canal on the total sound pressure level and sound pressure levels at different frequencies were studied.Also, 3 SSPLs that are usual in the workplaces were used in this research.The ear canal can amplify the sounds and increase the sound pressure levels.This amplification is found to be greater in men than in women.The resonance ability of the ear canal is larger in some frequencies, especially in higher frequencies.This ability of the ear canal should be considered in the workplace noise evaluations.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.368
Teacher spread0.333 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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