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Record W1606273442

Eliciting individual language describing differences in auditory imagery associated with four multichannel microphone techniques

2007· article· en· W1606273442 on OpenAlexafffundvenue
William L. Martens, Sungyoung Kim, Kent Walker, David Benson, Wieslaw Woszczyk

Bibliographic record

VenueCanadian acoustics · 2007
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaCentre for Interdisciplinary Research in Music Media and TechnologyMcGill University
KeywordsLoudspeakerMicrophoneActive listeningAcousticsCentroidSpeech recognitionComputer sciencePsychologyArtificial intelligenceCommunicationPhysics
DOInot available

Abstract

fetched live from OpenAlex

The individual language describing distinctions in identifiable auditory imagery associated with two short excerpts of four solo piano pieces with four multichannel microphone techniques is discussed. The 32 five-channel stimuli are presented through five active full bandwidth loudspeakers positioned at a height of l.2m from the floor and at a radius of 1.5 m from the central listening position. The bipolar elicited from the five listeners and the relative frequency with which they are generated are also summarized. The centroid response dataset is calculated from the combined ratings for 8 of the musical programs for each of the four microphone techniques. This analysis for one listeners gave a tight cluster between obtained ratings on three attribute scales while the second tight cluster is observed between ratings on the scale.

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 score0.004
Threshold uncertainty score0.012

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 communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.250
Teacher spread0.201 · 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
Published2007
Admission routes3
Has abstractyes

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