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Record W2508066889

Closed door flight test investigation of cabin noise exposure in the NRC Bell 412 helicopter

2014· article· en· W2508066889 on OpenAlexaff
Sebastian Ghinet, Andrew Price, Marc Alexander, Anant Grewal, Viresh Wickramasinghe, Yong Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAircrewCrewAeronauticsNoise (video)Sound pressureAircraft noiseEngineeringNoise exposureHearing protectionAcousticsAutomotive engineeringHearing lossAudiologyEnvironmental scienceComputer scienceTelecommunicationsNoise reductionMedicine
DOInot available

Abstract

fetched live from OpenAlex

Aircraft noise affects crew communication and comfort and continued exposure can lead to hearing loss if the personnel hearing protection is insufficient or improperly worn. Therefore, evaluation of the noise levels experienced by helicopter aircrew is essential in order to select optimum hearing protection solutions to enhance aircrew safety and reduce the potential for health issues in the long term. Cabin noise exposure of crew members was investigated in the NRC Bell 412 helicopter, the civilian variant of the CH-146 employed by the RCAF and the results were compiled with regard to the acoustic specifications of hearing protection devices in use during missions. The tests were conducted in accordance with standard test procedures ISO 5129:2001 “Acoustics - Measurement of sound pressure levels in the interior of aircraft during flight” and MIL-STD-1294A “Acoustical noise limits in helicopters”. Acoustic sound pressure levels (SPL) were recorded in the aircraft interior at different crew station locations during various flight conditions.

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)0.002
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 score0.285
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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
Published2014
Admission routes1
Has abstractyes

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