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Record W4224104664 · doi:10.1080/14680629.2022.2060123

Performance evaluation of cold in-place recycling materials through a simple semi-circular bending test

2022· article· en· W4224104664 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

VenueRoad Materials and Pavement Design · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsIntertek (Canada)
FundersMinnesota Department of Transportation
KeywordsAsphaltTest methodBendingEmulsionMaterials scienceCrackingStructural engineeringComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

Cold in-place recycling (CIR) is a method of recycling existing Hot Mix Asphalt (HMA) pavements. In this study, a new performance-based test method, called Fracture Index Value for Energy (FIVE), is developed to capture the fracture energy of the CIR materials. Furthermore, various stabilisation agents were used including engineered emulsion (EE), high float asphalt emulsion (HFMS-2s), commodity asphalt emulsion (CSS-1) and foamed asphalt. The FIVE test was first validated using disk-shaped compact tension (DCT) test and then was implemented on the study mixtures at their optimum stabilisation agent contents. The ranking of the mixtures and reproducibility of the FIVE test was proved to be true through inter-lab comparisons of the test results between two independent testing laboratories. The statistical testing results postulate that the FIVE test is a simple test method that is capable of characterising the cracking performance of the CIR mixtures.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.052
GPT teacher head0.276
Teacher spread0.224 · 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