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Record W2507783039

Modular Expansion Joint Noise in B.C.

2011· article· en· W2507783039 on OpenAlexaffvenueabout
Duane E. Marriner, Clair Wakefield

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsOctave bandNoise (video)Offset (computer science)EngineeringRange (aeronautics)Modular designTraffic noiseAcousticsOctave (electronics)PhysicsComputer scienceNoise reductionAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT The passage of a vehicle over a Modular Expansion Joint (MEJ) is known to generate noise.  On August 23-26, 2009, emissions from two MEJ’s were investigated in the Province of British Columbia, Canada.  MEJ emissions were monitored at wayside with the passage of general traffic, for a range of longitudinal offset positions.  MEJ widths were 1.2 and 2.6 m.  Variations in the character of tire/joint interaction noise were subjectively observed for several vehicle classes, weights, speeds, tire widths and tire types.  For general traffic with an average speed of 100 kph, it was found that the dominant MEJ emission frequency was centered at the 630 Hz one-third octave band in 73% of cases.  On March 3-4, 2010, three vehicles were used to conduct controlled tests and the relationship between the character of MEJ emissions and speed was explored for speeds of 90 to 100 kph.  It was found that the dominant frequency was 630 Hz in 69% of the trials independent of speed.  For the heaviest vehicles, MEJ emission levels were up to 8.7 dB higher.  MEJ emissions were found to exhibit a directional variation of L max 8.6 dBA at a range of 100 m. Keywords: Modular Expansion Joint, Tire, Joint, Noise, Emission, Monitoring, Controlled Vehicle Test, Using Instrumentation in Innovative Ways

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.985

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 head0.182
Teacher spread0.160 · 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

Citations0
Published2011
Admission routes3
Has abstractyes

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