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Record W1966330037 · doi:10.1115/imece2009-11477

Parametric Influence on the Sound Absorption Coefficient of Porous Asphalt

2009· article· en· W1966330037 on OpenAlexaff
Punnamee Sachakamol, Liming Dai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsNoise reduction coefficientParametric statisticsAttenuation coefficientAbsorption (acoustics)AcousticsAcoustic impedanceNoise controlElectrical impedanceTraffic noiseMaterials sciencePorosityNoise (video)AsphaltPorous mediumSound (geography)MechanicsComputer scienceEngineeringComposite materialOpticsPhysicsNoise reductionMathematicsStatisticsUltrasonic sensor

Abstract

fetched live from OpenAlex

This research intends to develop a method for predicting the sound absorption coefficient of various porous highway pavement materials. Since many of the existing prediction models for acoustic properties and traffic noise still have limitations and problems with accuracy, sound absorption coefficients are measured with the impedance tube method to verify numerical values obtained from the model. Results obtained from the experiment and numerical simulations are compared and presented to reveal the effect and influence of the control parameters.

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: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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
Published2009
Admission routes1
Has abstractyes

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