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Record W2624776908 · doi:10.4050/f-0072-2016-11421

Evaluation of Aircrew Noise Exposure Levels on a Canadian CH-147F Chinook Helicopter

2016· article· en· W2624776908 on OpenAlexaboutno aff
Andrew Price, Sebastian Ghinet, Yong Chen, Viresh Wickramasinghe, Anant Grewal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsnot available
Fundersnot available
KeywordsAircrewChinook windAeronauticsNoise (video)Environmental scienceAerospace engineeringEngineeringMeteorologyComputer sciencePhysicsFisheryBiology

Abstract

fetched live from OpenAlex

The cabin and cockpit noise levels of a Royal Canadian Air Force CH-147F Chinook medium to heavy lift utility helicopter were evaluated in this study. The sound pressure levels were measured at nine aircrew locations through 43 unique and representative flight and ground conditions in accordance with the ISO 5129:2001 standard. Additionally, the performance of a combination of currently in service helmets and headsets were evaluated in accordance with the ANSI Standard S12.42. The hearing protection performance results were used in combination with the measured sound pressure levels to evaluate the performance of the hearing protection in the context of the CH-147F noise environment. Results showed that the David Clark headsets equipped with active noise reduction provided the most superior hearing protection. The maximum exposure limit duration was calculated for each microphone location, hearing protector performance and flight condition combination. It was found that the David Clark headsets provided sufficient protection for an unlimited duration of exposure for an individual with a properly fitted headset. It was also found that improperly fitted hearing protection could result in an increased risk of hearing damage after merely 18 seconds.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.999

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)0.0020.000

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 head0.030
GPT teacher head0.241
Teacher spread0.210 · 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.

Study designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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