MétaCan
Menu
Back to cohort
Record W3211486091 · doi:10.1139/cjce-2021-0186

Performance comparison of asphalt emulsion stabilized granular base modified with cement or asphaltenes

2021· article· en· W3211486091 on OpenAlexafffundvenueabout
Muhammad Misbah Uddin, Farshad Kamran, Leila Hashemian

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of AlbertaCanadian Natural Resources
FundersAlberta Innovates
KeywordsAsphaltAsphalteneUltimate tensile strengthMaterials scienceCementEmulsionComposite materialCrackingFiller (materials)Chemical engineeringEngineering

Abstract

fetched live from OpenAlex

Asphalt emulsion is a common material used for pavement base course stabilization, and cement is usually added as an active filler to improve the stability of asphalt emulsion mixtures further. However, using cement in these mixes has several drawbacks, including high material costs and environmental issues. On the other hand, asphaltenes is a waste by-product derived from the processing of Alberta oil-sands bitumen that could be used for the same purpose. This investigation compares the impact of cement and asphaltenes as additives to asphalt emulsion-stabilized layers. To compare the performance properties, cement- and asphaltenes-modified mixtures are prepared at different concentrations. The performance properties of the modified mixtures are investigated by conducting a series of tests including Marshall stability, indirect tensile strength, IDEAL-CT, and tensile strength ratio. In addition, to evaluate low-temperature cracking resistance of the mixtures, indirect tensile strength test is conducted at 0 °C and –10 °C.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.800

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 head0.026
GPT teacher head0.233
Teacher spread0.208 · 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

Citations7
Published2021
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207