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Record W1485474031

Use of psychoacoustic metrics for the analysis of next generation computer cooling fan noise

2005· article· en· W1485474031 on OpenAlexaffvenue
Colin Novak, Helen Ule, Robert Gaspar, R. Haven Wiley

Bibliographic record

VenueCanadian acoustics · 2005
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychoacousticsSound qualityAcousticsSound pressureNoise (video)Computer scienceSound powerVisualizationQuality (philosophy)Directional soundSound (geography)Acoustic emissionPerceptionSpeech recognitionEngineeringArtificial intelligencePhysics
DOInot available

Abstract

fetched live from OpenAlex

The validity of using several psychoacoustic metrics for acoustic analysis of two different cooling solutions was investigated. Psychoacoustics involves the quantitative evaluation of subjective sensations using sound quality metrics. Applying sound quality metrics allow the visualization of the complicated relationship between the physical and perceptual acoustic quantities. Testing of both the sound pressure level and sound power level results indicate a linear increase in acoustic emission levels. Traditional analytical approaches also quantify the amplitude of acoustic emissions, but offer no idea of the quality of sound produced.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.060
GPT teacher head0.244
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations2
Published2005
Admission routes2
Has abstractyes

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