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Record W4402501788 · doi:10.11159/icceia24.152

An Investigation on OGFC Mix Made with VG-40 and CRMB-55 Binders

2024· article· en· W4402501788 on OpenAlexvenueno aff
Pavan Kumar Sharma, Shivani Shukla, Mahabir Panda

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The present study exhibits the effect of different bitumen binders on the open-graded friction course (OGFC) mix prepared using Indian Road Congress (IRC) OGFC aggregate gradation.The two different bitumen binders, VG-40 bitumen and CRMB-55 binder were used to prepare the mixes which were compacted at 80 gyrations in a Superpave Gyratory Compactor (SGC).Different tests were utilized to evaluate the optimum binder content (OBC) and performance characteristics of the mix.The tests conducted to estimate the OBC are the Marshall test, Cantabro abrasion loss test, drain down test, and moisture susceptibility test, while the performance evaluation of the OGFC mix was examined using the wheel tracking rut depth test and the falling head permeability test.The OBC for a mix prepared with binder VG-40 and CRMB-55 was found to be 3.5% and 4.0%, respectively.Compared to VG-40, the mix prepared with CRMB-55 exhibited to perform better with more resistance to rut depth and less permeability at OBC.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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