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Record W2516480338

CSA Z107.6 Audiometric Testing for use in Hearing Loss Prevention Programs: A new title for a new edition

2016· article· en· W2516480338 on OpenAlexafffundvenueabout
Sasha Brown, Christian Giguère, Michael Sharpe

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsWorkers Compensation Board of British Columbia
FundersMinistère de la Défense NationaleWorkSafeBCUniversity of Ottawa
KeywordsTechnicianHearing lossTest (biology)AudiologyEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

The most recent addition to the Canadian Standards Association’s (CSA) Occupational Hearing Conservation Technical Committee’s fleet of standards is a long-awaited second edition of CSA Z107.6, Audiometric Testing for use in Hearing Loss Prevention Programs. This Standard focuses on quantifying hearing loss as a means of early detection of possible damage from hazardous noise exposure and is directed to organizations and service providers responsible for conducting audiometric tests as part of a Hearing Loss Prevention Program (HLPP). The new edition is intended to be used in conjunction with CSA Z1007, Management of Hearing Loss Prevention, and incorporates several amendments and additions to the original 1990 edition. The 2016 edition now includes: mobile audiometric test facility requirements; an expanded section on audiometric technician training requirements; and guidance to the technician on classifying test results, advising the tested individual about their test results and providing reports to the Hearing Loss Prevention Program Administrator for use within an HLPP.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.174
GPT teacher head0.395
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2016
Admission routes4
Has abstractyes

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