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Micro-Mechanism Study and Road Engineering Application of Asphalt Rubber

2013· article· en· W2013912910 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

VenueApplied Mechanics and Materials · 2013
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsAsphaltNatural rubberMaterials scienceComposite materialCrumb rubberRaw materialMechanism (biology)ChemistryPhysics

Abstract

fetched live from OpenAlex

Based on the results of electron microscope observation, the formation mechanism of asphalt rubber can be explained as followed: both chemical and physical reaction take place when crumbed rubber and base bitumen are mixed together. Physical swelling of the crumbed rubber particles take place during the process, at the same time a layer of gel comes into being on the surface of the crumbed particles, through which the particles connect with each other and form a network structure. Because of the unique formation mechanism of asphalt rubber, the material provide some much better properties than other kinds of bitumen. The main points of choosing raw material and production technology of asphalt rubber is given out based on the results of a series of contrast tests such as different sizes of crumbed, different crumbed rubber content, different reaction time and different reaction temperatures. The good performance of asphalt rubber in the field of road engineering is proven based on the test results of asphalt rubber premix and road pavement.

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 score0.073
Threshold uncertainty score0.596

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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