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Record W2850969266 · doi:10.11159/ijci.2018.002

Residents’ Perceptions on Road Hump Effectiveness in Malaysian Residential Environments

2018· article· en· W2850969266 on OpenAlexvenueno aff
Khairun Sarah Radhiah Bachok, Abdul Azeez Kadar Hamsa, Mohd Zin Mohamed, Mansor Ibrahim

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2018
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
FundersKementerian Sains, Teknologi dan Inovasi
KeywordsPerceptionTransport engineeringPsychologyGeographyApplied psychologyEnvironmental healthMedicineEngineering

Abstract

fetched live from OpenAlex

Two common concerns for residents in their respective residential areas are high traffic speeds and noise levels. This paper analyses the perception of residents on the effectiveness of road humps in improving residential living environment based on their effects on traffic speed and noise. The study was conducted in three Malaysian residential areas, specifically Putrajaya, Taman Setiawangsa, and Keramat. A questionnaire survey was distributed to 478 respondents asking for their perceptions on whether road humps have improved their living environment by reducing speed and noise. Binary logistic regression models were also developed to identify variables that affect respondents' likelihood to agree that existing road humps have improved the living environment of the residents by reducing traffic speed and noise. The result of this study is that the height of road humps affects noise levels and traffic speeds, as average vehicle speeds and LAeq were lowest at the road hump of 90mm height. The respondents' perceptions of the importance of lower traffic speeds and noise levels, as well as the appropriateness of hump heights and locations, were significant variables in determining the likelihood of their agreement that road humps have improved their living environment.

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 score0.638
Threshold uncertainty score0.742

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 science0.0010.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.241
Teacher spread0.237 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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