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Record W6948963728 · doi:10.5281/zenodo.10319599

Effect of Coated Aggregates by Plastic Bottles on Bituminous Concrete for Road Pavement

2023· article· en· W6948963728 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicMedicinal Plant Pharmacodynamics Research
Canadian institutionsWeyerhauser (Canada)
Fundersnot available
KeywordsAsphaltAggregate (composite)Filler (materials)Specific gravityPlastic wasteAsphalt concrete

Abstract

fetched live from OpenAlex

The amount of waste plastics which are released by factories and human activities day to day becomes big problem to human being health as days ahead. The major impact of waste plastics is the environmental pollution, as they do not decompose. This current study, was about to use those waste plastics as Partial replacement of bitumen in bituminous concrete production for road pavement. Different materials like plastics, coarse aggregate, fine aggregate, filler materials and bitumen were used. The plastics used in this research were shredded into small pieces of 5 to 10 mm size and mixed with aggregates in hot state. Tests on aggregates and bitumen for strength and specific gravity were carried out respectively. To examine the behavior of plastics, test specimens were prepared without plastic contents with different bitumen percentage of 4.5%, 5%, 5.5%, 6%, 6.5% for the purpose of finding optimum bitumen binder for normal mix. The optimum bitumen percent for normal bituminous mix is 5.7% and the maximum stability of 9.2875 KN is reported, which is above 8.2 KN as minimum stability accepted. The quantity of bitumen was partially replaced with 3%, 10%, 15% and 20% by plastics. High stability value of 10.89 KN was found at optimum 14.3% plastics. This paper reports that waste plastics produce a good result compare to normal mix.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.084
GPT teacher head0.395
Teacher spread0.310 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicMedicinal Plant Pharmacodynamics ResearchFrench-language works237,207