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Record W4381057128 · doi:10.3397/nc_2023_0172

Characterization of Road noise levels increase due to installation of transverse rumble strips

2023· article· en· W4381057128 on OpenAlexaff
Kareem Aly, John Vairo, Mervyn Choy

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsRumbleSTRIPSNoise (video)Transverse planeAcousticsNoise controlEngineeringPhysicsElectrical engineeringStructural engineeringComputer scienceNoise reduction

Abstract

fetched live from OpenAlex

Transverse rumble strips have been widely employed as a means to warn distracted. Noise emissions from traveling over the rumble strips negatively impact surrounding communities. Moreover, the distinguished noise characteristic of rumble strips makes this noise a nuisance to the surrounding community. Measurements of the noise emission generated from vehicles passing over transverse rumble strips have been completed adjacent to the 18-rumble strips. To determine the impact of the rumble strips on noise generation, sound level measurements were taken of vehicles traveling over smooth pavement prior to travelling over the rumble strips shortly after. The measurements were completed for uncontrolled normal road traffic and for controlled vehicle pass-by, where the vehicle speed was steady and controlled. The overall noise emission from the transverse rumble strips results with a 3-9 dB increase above the corresponding vehicle noise emission Leq on smooth pavement. The corresponding sound exposure level is calculated. The increase in the noise emission is largely dependent on the vehicle type and not correlated to vehicle speed. The rumble strip noise is characterized as quasi-steady impulsive sound for receptors, which may require an additional noise penalty. The results show that the rumble strips noise does not have any distinct tone.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.046
GPT teacher head0.340
Teacher spread0.294 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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