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Record W2995382026 · doi:10.1080/14992027.2019.1698067

Influence of impulse noise on noise dosimetry measurements on the International Space Station

2019· article· en· W2995382026 on OpenAlexaff
Ann Nakashima, José G. Limardo, Andrew Boone, Richard W. Danielson

Bibliographic record

VenueInternational Journal of Audiology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsImpulse noiseCrewImpulse (physics)Noise (video)Parametric statisticsInternational Space StationNoise exposureAcousticsStatisticsNoise measurementMathematicsComputer scienceMedicineNoise reductionPhysicsAudiologyEngineeringAeronautics

Abstract

fetched live from OpenAlex

Objective: Acoustic dosimetry (AD) data collected on the International Space Station (ISS) were analysed to investigate the impact of impulse noise on crew noise exposure.Design: The noise exposure during work (LAeq16h) and sleep (LAeq8h) time, and the number of impulses >115 dB peak that occurred during each measurement activity, were calculated from the AD data. Two parametric studies were used to estimate the effect of 1) impulses in the original data set, and 2) hypothetical impulses of different levels, durations and quantities on LAeq16h.Study sample: Twelve sets of AD data collected on the ISS from November 2017 to October 2018.Results: The ISS work time noise limit (72 dBA) was exceeded in four of the 12 data sets. In three of those, there were over 100 impulses >115 dB peak and the number of impulses was significantly correlated with LAeq16h. However, the impulses only caused a meaningful increase in LAeq16h when the number of occurrences was large (>50), or when both the level and duration of the impulses were large.Conclusions: Continued monitoring of impulse noise data is recommended to facilitate the investigation of exceedances or abnormalities in future AD data acquired on the ISS.

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)0.001
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 score0.375
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.390
Teacher spread0.352 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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