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Record W2993536946

Use of portable audio devices by university students

2007· article· en· W2993536946 on OpenAlexaffvenue
Shazia S. Ahmed, Sina Fallah, Brenda Garrido, Andrew J. Gross, Matthew King, Timothy W. Morrish, Desiree Pereira, Shaun Sharma, Ewelina Zaszewska, M. Kathleen Pichora‐Fuller

Bibliographic record

VenueCanadian acoustics · 2007
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsAmorfix (Canada)
Fundersnot available
KeywordsSession (web analytics)Noise (video)Computer scienceBackground noiseActive listeningSound qualityMultimediaSpeech recognitionTelecommunicationsPsychology
DOInot available

Abstract

fetched live from OpenAlex

New digital portable audio devices such as the Apple iPod have caused renewed concerns that recreational noise exposure may pose a danger to the hearing health of young adults. In this study, 150 undergraduates completed a survey about their use of portable audio devices and about other factors that could affect their hearing health. In addition to completing the survey, 24 students also participated in an experimental session. In the experimental session, hearing thresholds up to 14 kHz were measured and objective acoustical measures of output of the iPod were obtained. Participants listened to music and adjusted an iPod to their preferred setting in five conditions: in quiet and in two types of background noise, traffic or multi-talker babble background, at a high and a low level. A Bruel and Kjaer dummy head and PULSE sound analysis system were used to measure the output of the iPod at the preferred settings of the students and at predetermined volume and equalizer control settings. It was found that most students use portable audio devices, but the pattern of their usage seems to be potentially hazardous only for a minority. The importance of education about safe usage of this technology is emphasized.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.040
GPT teacher head0.346
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2007
Admission routes2
Has abstractyes

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