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Record W2016769890 · doi:10.1525/mp.2009.27.1.17

Identification of TV Tunes by Children with Cochlear Implants

2009· article· en· W2016769890 on OpenAlexaff
Tara Vongpaisal, Sandra E. Trehub, E. Glenn Schellenberg

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2009
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCochlear implantMelodyLyricsFlutePsychologyMusic perceptionIdentification (biology)AudiologySet (abstract data type)PerceptionSpeech recognitionMusicalComputer scienceArtMedicineVisual artsLiteratureNeuroscience

Abstract

fetched live from OpenAlex

INTRINSIC PITCH PROCESSING LIMITATIONS OF cochlear implants constrain the perception of music, particularly melodies. We tested child implant users' ability to recognize music on the basis of incidental exposure.Using a closed-set task, prelingually deaf children with implants and hearing children were required to identify three renditions of the theme music from their favorite TV programs: a flute rendition of the main (sung) melody, a full instrumental version without lyrics, and the original music. Although child implant users were less accurate than hearing children, they successfully identified all versions of songs at above-chance levels——a finding that contradicts widespread claims of child and adult implant users' difficulties with melody identification.We attribute their success primarily to timing cues that match those of the original music.

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)0.007
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.322
Teacher spread0.298 · 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

Citations33
Published2009
Admission routes1
Has abstractyes

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