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Record W2516797923

Effectiveness of Noise Reducing Asphalt Pavements

2016· article· en· W2516797923 on OpenAlexvenueaboutno aff
Scott Penton

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsNoise (video)AsphaltChristian ministryNoise reductionAcousticsTraffic noiseRoad surfaceEnvironmental scienceNoise controlNoise measurementRoadway noiseEngineeringAutomotive engineeringSound intensityRoad trafficIntensity (physics)Marine engineeringSound (geography)Computer scienceCivil engineeringMaterials scienceTransport engineeringOpticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

As vehicles pass over a roadway surface, noise is created due to the interaction between the tires and the pavement surface.For most vehicles travelling at speeds in excess of 60 km/h, the majority of the noise emitted by the vehicle comes from the tires.Noise reducing asphalts operate by affecting the noise generation mechanisms, and through providing an acoustically absorptive surface for sound waves travelling outward from the vehicle.On-board sound intensity measurements were used to evaluate reductions in tire noise for several pavement types, as part of testing for the Ontario Ministry of Transportation.Measurements were conducted over several years to quantify the longevity of any measured noise reduction.Comparisons are made versus other published test results.

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)0.002
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 score0.988
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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