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Record W1508066472 · doi:10.1080/15459620490432178

Noise Exposure of Music Teachers

2004· article· en· W1508066472 on OpenAlexaff
Alberto Behar, Ewen MacDonald, Jason Lee, Jie Cui, Hans Kunov, Willy Wong

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2004
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNoise exposureNoise (video)AcousticsNoise levelAudiologyHearing lossPsychologyMathematicsComputer sciencePhysicsMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

A noise exposure survey was performed to assess the risk of hearing loss to school music teachers during the course of their activities. Noise exposure of 18 teachers from 15 schools was measured using noise dosimeters. The equivalent continuous noise level (Leq) of each teacher was recorded during single activities (classes) as well as for the entire day, and a normalized 8-hour exposure, termed the noise exposure level (Lex) was also computed. The measured Leq exceeded the 85-dBA limit for 78% of the teachers. Lex exceeded 85 dBA for 39% of the teachers. Limited recommendations on how to reduce the noise exposures are provided. The need for a hearing conservation program has also been emphasized.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.330

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 head0.033
GPT teacher head0.329
Teacher spread0.296 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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