MétaCan
Menu
Back to cohort
Record W2115613213 · doi:10.1177/1084713812471586

Musicians and Hearing Aid Design—Is Your Hearing Instrument Being Overworked?

2012· article· en· W2115613213 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTrends in Amplification · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsEmmanuel Bible College
Fundersnot available
KeywordsHearing aidMicrophoneActive listeningNoise (video)AudiologyComputer scienceDistortion (music)AcousticsSoftwareSpeech recognitionMedicineAmplifierTelecommunicationsPsychologyCommunicationPhysicsBandwidth (computing)Artificial intelligenceSound pressure

Abstract

fetched live from OpenAlex

Music can have sound levels that are in excess of the capability of most modern digital hearing aids to transduce sound without significant distortion. One innovation is to use a hearing aid microphone that is less sensitive to some of the lower frequency intense components of music, thereby providing the analog-to-digital (A/D) converter with an input that is within its optimal operating region. The "missing" low-frequency information can still enter through an unoccluded earmold as unamplified sound and be part of the entire music listening experience. Technical issues with this alternative microphone configuration include an increase in the internal noise floor of the hearing aid, but with judicious use of expansion, the noise floor can significantly be reduced. Other issues relate to fittings where significant low-frequency amplification is also required, but this type of fitting can be optimized in the fitting software by adding amplification after the A/D bottle neck.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.166
GPT teacher head0.344
Teacher spread0.178 · 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