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Record W2610091062 · doi:10.3390/coatings7050058

Field Evaluation of Red-Coloured Hot Mix Asphalt Pavements for Bus Rapid Transit Lanes in Ontario, Canada

2017· article· en· W2610091062 on OpenAlexaffabout
Qingfan Liu, Sina Varamini, Susan Tighe

Bibliographic record

VenueCoatings · 2017
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMcAsphalt Industries (Canada)University of Waterloo
Fundersnot available
KeywordsAsphaltAsphalt pavementMetropolitan areaEnvironmental scienceTransport engineeringCivil engineeringForensic engineeringField surveyEngineeringGeographyCartographyArchaeology

Abstract

fetched live from OpenAlex

Coloured pavements have been implemented by metropolitan areas to denote dedicated lanes for bus rapid transit to maintain a high level of safety. Transit benefits of these installations are well documented. However, field performance of various types of coloured pavement has not been investigated systematically, with questions not being answered. In collaboration with the Regional Municipality of York (ON, Canada) where red pavement sections have been in operation for years for its bus rapid transit lanes, the Centre for Pavement and Transportation Technology at the University of Waterloo (Waterloo, ON, Canada) assessed the performance of various types of red pavements including epoxy paint and red asphalt mixes. It was found that, with significant lower texture depth, epoxy paint surface has disadvantages to red asphalt pavement from a pavement texture and safety perspective. The red asphalt sections in this study were observed as lower yet compatible frictional levels to conventional black pavement. Various types of contamination onto the red pavement were observed during field survey. In addition, the ultraviolet radiation degraded the colour of red asphalt pavement over time and may make it less effective for lane designation. Long-term monitoring is recommended to evaluate the functional and structural performance of red asphalt pavement.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.041
GPT teacher head0.274
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

Citations10
Published2017
Admission routes2
Has abstractyes

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