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Record W2263275787

Canadian Usage and Practices for Evaluating Warm-Mix Asphalt Moisture Susceptibiliity

2016· article· en· W2263275787 on OpenAlexaboutno aff
Sina Varamini, Susan Tighe

Bibliographic record

VenueTransportation Research Board 95th Annual MeetingTransportation Research Board · 2016
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltGreenhouse gasEngineeringGeological surveyTransport engineeringEnvironmental scienceCivil engineeringBusinessGeography
DOInot available

Abstract

fetched live from OpenAlex

Warm Mix Asphalt (WMA) technology is gaining more acceptance by transportation agencies in Canada due to the pressures from environmental agencies and environmental acts to reduce Green House Gas (GHG) emissions generated by production and placement of paving mixtures. However, WMA technology has certain functional considerations, namely moisture susceptibility, that need to be addressed in order to ensure it meets performance requirements in addition to providing environmental benefits. To identify possible gaps in the current understanding and performance of WMA, a Canada-wide survey has been prepared at the Centre for Pavement and Transportation Technology (CPATT) located at the University of Waterloo to document the Canadian state-of-the-art related to WMA technologies. This paper presents the results of this survey on the preferred practices conducted by various Canadian transportation agencies. Furthermore, these results are compared with the most recent survey of the United States Departments of Transportation (DOTs) and industry conducted as part of the National Cooperative Highway Research Program (NCHRP) project 09-49.

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.100
GPT teacher head0.426
Teacher spread0.326 · 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.

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
Published2016
Admission routes1
Has abstractyes

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