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Record W103982402

Evaluation of Reclaimed Asphalt Pavement and Virgin PG Binder Blends

2009· article· en· W103982402 on OpenAlexaboutno aff
J K Davidson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltChristian ministryAsphalt pavementRutEnvironmental scienceWaste managementForensic engineeringEngineeringCivil engineeringMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

With the switch to Performance Graded Asphalt Cements (PGAC) from the traditional penetration and viscosity grading in Ontario, the use of Reclaimed Asphalt Pavement (RAP) has decreased dramatically. Currently, hot mix contractors tend to use only 20 percent RAP or less in their mixes. This is due to the directive from the Ministry of Transportation of Ontario (MTO) that allows contractors to use up to 20 percent RAP in their mixes without the need to change the grade of virgin asphalt cement. The MTO would like to increase the percentage of RAP in all parts of the Province, however very little research has been completed to guide the selection of PGAC for use with recycled mixes. The objective of this laboratory study was to determine how the various grades of PGAC react when combined with different percentages of RAP with respect to the final performance grade of the blend. Two RAP sources and ten virgin PGAC binders were investigated. The virgin binders were blended with the recovered RAP binders in various percentages and tested according to the American Association of State Highway and Transportation Officials (AASHTO) M320 specification. Based on the results obtained, various conclusions and recommendations are presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.285
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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
Published2009
Admission routes1
Has abstractyes

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