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Record W2164737909 · doi:10.1061/47624(403)1

Gradation and Performance Research of Cold Recycled Mixture

2011· article· en· W2164737909 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsGradationProcess engineeringEnvironmental scienceComputer scienceWaste managementMaterials scienceArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Cold in-place recycling was adopted for a project in China due to the availability of reclaimed asphalt pavement (RAP). Based on the Foshan loop project in Guangdong, the gradation design of cold recycled mixtures (CRM) was optimized by the Bailey Method. Emulsified asphalt and cement were used as additives. Then, the screenings of aggregates in RAP and RAP were analyzed and compared. Additionally, new aggregates and cement were added to dispose the framework structure of the cold recycled mixture, and modified Marshall Tests conducted to determine the optimum amount of emulsified asphalt and water, by which cold recycled mixture was formed and performance experiments of asphalt mixture carried out. Eventually, the results show that the gradation design of cold recycled mixture needs to be adjusted by the screening of aggregates in RAP. Also, cold recycled mixture is suitable for highway sub-grade and pavements of low-grade roads.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.259

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 head0.064
GPT teacher head0.259
Teacher spread0.195 · 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

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

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