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Record W2146278609 · doi:10.3109/14992027.2011.622303

Effective masking levels for bone-conducted amplitude- and frequency-modulated tones in adults with normal hearing: A behavioural study

2011· article· en· W2146278609 on OpenAlexafffund
Susan A. Small, Erin E. Hansen

Bibliographic record

VenueInternational Journal of Audiology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAudiologyMasking (illustration)MedicineBone conductionAmplitudePhysics

Abstract

fetched live from OpenAlex

OBJECTIVES: (1) to estimate the amount of masking needed to eliminate perceptual responses to 1000- and 4000-Hz bone-conducted mixed amplitude- (AM) and frequency-modulated (FM) tonal stimuli in adults with normal hearing, and (2) to compare these findings to recently reported effective masking levels (EMLs) for bone-conducted 80-Hz ASSRs. DESIGN: Stimuli were bone-conducted single sinusoidal tones with carrier frequencies of 1000 and 4000 Hz (Mixed modulation (MM): 100% AM & 25% FM at 85-101 Hz) presented to the temporal bone at 15-45 dB HL for 1000 Hz and 25-35 dB HL for 4000 Hz. Air-conducted 1- and 4-Hz narrow-band noise maskers were presented to both ears simultaneously using ER-3A insert earphones. EMLs for each of the stimuli were determined behaviourally. STUDY SAMPLE: Seventeen adults (mean age: 27.6 years) with normal hearing participated. RESULTS: Overall, EMLs were 10-17 dB higher for perceptual responses compared to ASSRs for 1000 and 4000 Hz. Linear regression analyses revealed that behavioural and ASSR EMLs were not significantly correlated for most of the stimuli presented except for 1000-Hz presented at 45 dB HL (r =.64, p = .013). CONCLUSIONS: EMLs are frequency- and testing method-dependent for bone-conducted MM tonal stimuli for normal-hearing adults.

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)0.001
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 score0.131
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.073
GPT teacher head0.324
Teacher spread0.251 · 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

Citations3
Published2011
Admission routes2
Has abstractyes

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