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Record W4385563151 · doi:10.1139/cjce-2022-0467

Performance evaluation of petroleum hard pitch modified with rice straw bio-oil

2023· article· en· W4385563151 on OpenAlexvenueno aff
Muskan Verma, Bharath Gottumukkala, Ambika Behl, Vijay Kakade, Jitendra Kumar

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltMaterials scienceRheologyRutViscosityUltimate tensile strengthComposite materialPetroleumDynamic modulusMoistureDynamic shear rheometerEnvironmental scienceStrawPulp and paper industryDynamic mechanical analysisPolymerGeologyEngineeringChemistry

Abstract

fetched live from OpenAlex

In this present study, bio-binder was made using the bio-oil obtained from rice straw and petroleum hard pitch to reduce the air pollution caused by the burning of waste rice straw in the field. The physical, rheological, and chemical tests were conducted on the viscosity-grade binders (VG30 and VG40), petroleum hard pitch, and bio-binders prepared with a blending of petroleum hard pitch with 20 and 30 wt.%. The performance of bituminous mixes made with viscosity-grade binders and bio-binder was evaluated by using the dynamic creep, resilient modulus, and tensile strength tests. The results of the frequency sweep and Fourier transform infrared tests indicate that bio-binders have poor resistance to aging. The rut resistance of the bio-binder modified mix was better than that of the VG30 mix. There is no significant difference in moisture resistance of bituminous mixes prepared with bio-binder and viscosity-grade binders.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.014
GPT teacher head0.188
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Civil EngineeringSame topicLignin and Wood ChemistryFrench-language works237,207