MétaCan
Menu
Back to cohort
Record W1971627297 · doi:10.1061/47634(413)28

Microstructure and Performance Analysis of Nanomaterials Modified Asphalt

2011· article· en· W1971627297 on OpenAlexaff
Hui Yao, Liang Li, Hua Xie, Han-Cheng Dan, Xiao‐Li Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsAsphaltMaterials scienceNanomaterialsSoftening pointComposite materialNanometreMicrostructureDuctility (Earth science)NanotechnologyCreep

Abstract

fetched live from OpenAlex

Nanomaterials have been generally applied in all fields all over the world, because of their distinctive physical and chemical characteristics. In order to improve the properties of asphalt and asphalt mixtures, the selection of nanometer materials for modifying the base asphalt was processed. Furthermore, a series of asphalt experiments were performed to analyze the basic performance of these asphalt with varying nanometer material contents. The tests included penetration, ductility, softening point, viscosity and atomic force microscope (AFM). Property comparison between modified asphalt and base asphalt was undertaken. Additionally, AFM images of modified asphalt at the optimum nanomaterials content were presented, which indicated that 2% carbon nano powders modified asphalt has an excellent uniformity structure in the base asphalt compared with microstructures of the other nanomaterials modified asphalt. Besides, mechanical performance tests of asphalt mixtures were carried out, and showed that the properties of nanomaterial modified asphalt were superior to those of the base asphalt.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.799

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)0.0010.000

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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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 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

Citations15
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicAsphalt Pavement Performance EvaluationFrench-language works237,207