MétaCan
Menu
Back to cohort
Record W2517761625

CSA Z1007: A new management standard from the Canadian Standards Association for the management of hearing loss prevention programs

2016· article· en· W2517761625 on OpenAlexaffvenueabout
Jeffrey M. Goldberg, Tim Kelsall, Alberto Behar, P Malek

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsToronto Metropolitan UniversityCanadian Standards Association
Fundersnot available
KeywordsHearing lossNoise-induced hearing lossSet (abstract data type)Best practicePresentation (obstetrics)Noise exposureNoise (video)BusinessComputer scienceEngineeringAudiologyMedicinePolitical scienceArtificial intelligenceLaw
DOInot available

Abstract

fetched live from OpenAlex

Canada has a fragmented set of norms for Hearing Conservation owing the 14 jurisdictions that set standards for these programs.  CSA Z1007, a new standard written with the support of Canadian regulators , seeks to establish norms, which can be used to manage these programs as hearing loss prevention programs. The difference between loss prevention and conservation is a proactive stance seeking to remove the causes of noise induced hearing loss before permanent hearing damage takes place. The standard covers all aspects of hearing loss prevention from detection of potential toxic noise through to proactive measures to determine if the program is being effective.  The standard is compatible with and reflective of CSA’s other standards in this area. The presentation will address the fragmented nature hearing loss prevention in Canada, how the standard seeks to address that, what proactive measures can be taken, and how to determine if the standard is being applied effectively. As well, there are several informative annexes addressing parts of the program in greater detail and Best Practices for those who wish to excel in this area. The standard was published in March of 2016, and is currently seeking adoption from the regulators that enabled its creation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.266
Teacher spread0.250 · 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

Citations1
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian acousticsSame topicEnvironmental and Social Impact AssessmentsFrench-language works237,207