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Record W4392984073 · doi:10.32920/25413340

Field Attenuation of Foam Earplugs

2024· preprint· en· W4392984073 on OpenAlexaff
Fran Copelli, Alberto Behar, Tina Ngoc Le, Frank Russo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAttenuationAcousticsMaterials sciencePhysicsOptics

Abstract

fetched live from OpenAlex

<p>Background: Hearing protection devices (HPDs) are often used in the workplace to prevent hearing damage caused by noise. However, a factor that can lead to hearing loss in the workplace is improper HPD fitting, and the previous literature has shown that instructing workers on how to properly insert their HPDs can make a significant difference in the degree of attenuation.</p> <p>Methods: Two studies were completed on a total of 33 Hydro One workers. A FitCheck Solo field attenuation estimation system was used to measure the personal attenuation rating (PAR) before and after providing one-on-one fitting instructions. In addition, external ear canal diameters were measured, and a questionnaire with items related to frequency of use, confidence, and discomfort was administered.</p> <p>Results: Training led to an improvement in HPD attenuation, particularly for participants with poorer PARs before training. The questionnaire results indicated that much HPD discomfort is caused by heat, humidity, and communication difficulties. External ear canal asymmetry did not appear to significantly influence the measured PAR.</p> <p>Conclusion: In accordance with the previous literature, our studies suggest that one-on-one instruction is an effective training method for HPD use. Addressing discomfort issues from heat, humidity, and communication issues could help to improve the use of HPDs in the workplace. Further research into the effects of canal asymmetry on the PAR is needed.</p>

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score0.438

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 head0.011
GPT teacher head0.242
Teacher spread0.232 · 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 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
Published2024
Admission routes1
Has abstractyes

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