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Record W1603913332

Cold Weather Paving Using Warm Mix Asphalt Technology

2008· article· en· W1603913332 on OpenAlexaboutno aff
S Manolis, T Decoo, P Lum, Massimo Greco

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsCompactionAsphaltAsphalt pavementEnvironmental scienceCold weatherCementHot weatherWaste managementEngineeringGeotechnical engineeringMeteorologyMaterials scienceComposite materialGeography
DOInot available

Abstract

fetched live from OpenAlex

Asphalt cement modified with HyperTherm was used to demonstrate that warm mix asphalt technology can be used to extend the paving season by facilitating paving in cold weather. HyperTherm was validated as an effective warm mix technology during an overlay project on Katimavik Road in the City of Ottawa. The project involved the City of Ottawa mew 4.75 mm FC1 mix intended for traffic level C (3 to 10 million Equivalent Single Axle Loads) placed at a nominal 25 mm thickness. PG 58-28 modified with HyperTherm was used as the asphalt cement. The warm mix was produced at about 120 degrees C at the plant and compacted at between 75 to 90 degrees C. Phase two of the study utilized the improved mix workability properties imparted by warm mix technology to extend the paving season in a cold weather application. Oxford Road 4 in the County of Oxford, Ontario, was paved in December with PG 58-28 modified with HyperTherm. The mix was produced at conventional hot mix temperatures. Compaction targets were achieved despite the low ambient air temperatures and frozen granular grade by utilizing the improved workability imparted by the warm mix modifier.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.524

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 science0.0000.000
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 head0.030
GPT teacher head0.248
Teacher spread0.218 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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