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Record W1976466172 · doi:10.1139/l07-056

Study of crumb rubber materials as paving asphalt modifiers

2007· article· en· W1976466172 on OpenAlexvenueaboutno aff
D MacLeod, S Ho, Ryan Wirth, Ludo Zanzotto

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsCrumb rubberAsphaltCreepMaterials scienceGeotechnical engineeringWaste managementRutEnvironmental scienceCivil engineeringForensic engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Waste tire crumb rubber materials (CRM) were used to modify paving asphalts. The mixing time, hot-storage stability, Superpave grades, pumping and handling properties, phase separation tests, and repeated creep properties of the modified asphalts were studied using base asphalts of different hardness. Applying the Long-Term Pavement Performance (LTPP) program and the Transportation Association of Canada (TAC) model, optimal levels of CRM and suitable base asphalts were selected for the climatic conditions of Lethbridge, Alberta, Canada. High-temperature grade bumping protocol, regarding traffic volume and speed, was also considered. With joint efforts from the Tire Recycling Management Association of Alberta (TRMA), Husky Energy, and the City of Lethbridge, three test sections in different Lethbridge locations with various traffic volumes were paved from the years 2003 to 2005. So far, the City of Lethbridge is pleased with the initial performance of the test sections.Key words: waste tire, crumb rubber materials (CRM), paving asphalt, recycled, modification, Superpave, repeated creep, field test.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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 designSimulation or modeling
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

Citations30
Published2007
Admission routes2
Has abstractyes

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