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Record W284485175

The acoustic effects of diamond grinding Portland cement concrete pavement surfaces

2011· article· en· W284485175 on OpenAlexaboutno aff
S E Samuels

Bibliographic record

VenueRoad and transport research · 2011
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsDiamond grindingGrindingPortland cementDiamondForensic engineeringMaterials scienceEngineeringEnvironmental scienceCivil engineeringCementComposite materialGrinding wheel
DOInot available

Abstract

fetched live from OpenAlex

Transversely tyned Portland Cement Concrete (PCC) pavement surfaces generate very high levels of road traffic noise. A couple of years ago it was suggested, largely on the basis of experiences in the USA and Canada, that the introduction of narrow longitudinal grooves to these pavement surfaces would attenuate the noise. The longitudinal grooves would be applied through a process known as diamond grinding. Following that suggestion, diamond grinding was undertaken on several sections of PCC pavement surfaces in rural New South Wales (NSW) and in metropolitan Sydney. The diamond grinding was done for the NSW Roads and Traffic Authority (RTA) on roads under its jurisdiction as part of an ongoing development program. The present paper documents the conduct and outcomes of a series of investigations aimed at determining the acoustic effects of this diamond grinding process. The primary objectives of these investigations were to determine the road traffic noise characteristics of the original pavement surfaces investigated and to determine how these characteristics changed after the diamond grinding process. The outcomes of these investigations demonstrated, contrary to expectation, that the diamond grinding process had little or no effect on the road traffic noise generated on the pavement surfaces studied.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.271

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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 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

Citations2
Published2011
Admission routes1
Has abstractyes

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