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Record W3185521724 · doi:10.1121/2.0001436

Too loud! Non-occupational noise exposure causes hearing loss

2021· article· en· W3185521724 on OpenAlexaff
Daniel Fink, Jan Mayes

Bibliographic record

VenueProceedings of meetings on acoustics · 2021
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsDecibelHearing lossIndustrial noiseNoise (video)AudiologyHearing protectionNoise exposureAmbient noise levelEnvironmental noiseAbsolute threshold of hearingMedicineAcousticsComputer scienceSound (geography)

Abstract

fetched live from OpenAlex

That occupational noise exposure causes hearing loss has long been known, but non-occupational noise exposure was not recognized as a problem until the 1960s. Today, most Americans are regularly exposed to non-occupational noise sufficient to cause hearing loss, perhaps because of an erroneous belief that 85-decibel noise exposure, based on occupational standards, is safe for the public without time limit. Common noise sources include personal audio systems, especially among young people; public transit; social, sports, and entertainment venues; household appliances; and power tools and landscape maintenance equipment. As a result, approximately 25% of American adults age 20-69 have noise-induced hearing loss, 53% without significant occupational exposure. Why? The Equal Energy Hypothesis states that equal amounts of sound energy produce equal amounts of hearing loss, regardless of how that sound is distributed over time. The response to sound is non-linear, though, and brief high-level exposures may have disproportionate impacts on hearing. How loud is too loud? The Auditory Injury Threshold is only 75-78 A-weighted decibels. The Environmental Protection Agency calculated a time-weighted daily average of 70 decibels to prevent hearing loss, but the true safe noise level may be 60 decibels or lower. Recommendations are made to reduce public noise exposure.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.494
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.358
Teacher spread0.321 · 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 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

Citations17
Published2021
Admission routes1
Has abstractyes

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