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Record W2994102978

Rhythmic grouping and temporal gap discrimination

2011· article· en· W2994102978 on OpenAlexaffvenue
Tsuyoshi Kuroda, Emi Hasuo, Simon Grondin

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRhythmRepetition (rhetorical device)Tone (literature)Duration (music)PerceptionContrast (vision)AmplitudeSpeech recognitionAudiologyMathematicsCommunicationPsychologyAcousticsComputer sciencePhysicsArtificial intelligenceOptics
DOInot available

Abstract

fetched live from OpenAlex

The temporal sensitivity for discriminating a gap marked by two tones is affected by the structure of markers. It is known that successive tones are segmented to construct rhythm in perception, and the temporal sensitivity for repeated gaps is changed depending on what rhythmic grouping takes place. The amplitude of each tone rose and decayed during 20 ms at the beginning and the end with raised-cosine ramps. The markers were presented at a level that was 30 dB higher than the threshold level measured before each session. The repetition-pattern phase consisted of two sub-phases for two types of tasks, which were carried out in counterbalanced order. In the gap-following-short-tone task (RS), two repetition patterns were presented successively in each trial, and the second pattern was compared with the first pattern in terms of the gap duration following the short tones.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.262
Teacher spread0.163 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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