MétaCan
Menu
Back to cohort
Record W1796408578 · doi:10.1016/j.joto.2015.09.001

Improved hearing in noise using new signal processing algorithms with the Cochlear™ Nucleus® 6 sound processor

2015· article· en· W1796408578 on OpenAlexfundno aff
Jan Gilden, Kristen A. Lewis, Ginger Grant, Jillian Crosson

Bibliographic record

VenueJournal of Otology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
FundersYork UniversityNorthwestern UniversityUniversity of CincinnatiUniversity of Pennsylvania
KeywordsComputer scienceNucleusCochlear nucleusDigital signal processorCochlear implantNoise (video)Speech recognitionAlgorithmSignal processingArtificial intelligenceDigital signal processingComputer hardwareNeurosciencePsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Hybrid™ sound processors. METHODS: Eighty-two cochlear implant recipients (40 Nucleus 5 processor users and 42 Freedom Hybrid processor users) listened to and repeated AzBio sentences in noise with their current processor and with the Nucleus 6 processor. RESULTS: . CONCLUSION: The results of these studies demonstrate the superior performance and clinical utility of the SCAN algorithm in the Nucleus 6 processor over the Nucleus 5 and Freedom Hybrid processors.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.236

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 head0.083
GPT teacher head0.330
Teacher spread0.247 · 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 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

Citations12
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of OtologySame topicHearing Loss and RehabilitationFrench-language works237,207