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Record W4289754899 · doi:10.1080/14680629.2022.2106293

Impact of shear stress levels on validity of MSCR tests

2022· article· en· W4289754899 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 institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphaltRutShear stressMaterials scienceComposite materialShear (geology)Stress (linguistics)Direct shear testGeotechnical engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Although the MSCR test has become an improvement over the Superpave® |G*|/sinδ parameter, shear stress levels specified in the MSCR test might prove to be too low to successfully represent the stresses occurring in the pavement. To address this hypothesis, five conventional asphalt binders and a total of twelve polymer-modified asphalt blends were tested by MSCR at two different temperatures (50°C and 60°C) as well as five different shear stress levels of 0.1, 3.2, 6.4, 12.8, and 25.6 kPa. The rut results of hot mix asphalts were correlated with the MSCR results. Consequently, better correlations were obtained at higher shear stress levels used in performing MSCR. Moreover, it was shown that MSCR test overestimated the positive effects elasticity on the asphalts’ rut resistance and, thus, more consideration should be given to the asphalt’s ability to resist the applied stresses than to its elastic recovery.

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)0.001
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 score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.083
GPT teacher head0.303
Teacher spread0.220 · 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