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Record W2395959641

IMPROVEMENT OF CONCRETE DURABILITY BY COMPLEX MINERAL SUPER-FINE POWDER

2015· article· en· W2395959641 on OpenAlexaff
Bharitkar Dipali, Jayant Kanase, B. Tech Student

Bibliographic record

VenueInternational journal of advance research and innovative ideas in education · 2015
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsTrinity College
Fundersnot available
KeywordsDurabilitySilica fumeFly ashPozzolanMaterials scienceCementCompressive strengthGrindingAggregate (composite)GangueMetallurgyCalcinationPolymer concreteGround granulated blast-furnace slagSulfateProperties of concreteComposite materialPortland cementChemistry
DOInot available

Abstract

fetched live from OpenAlex

KG powder is a complex mineral superfine powder made by grinding the mix of calcined coal gangue and slag along with fly ash, silica fume in certain proportion. The concrete cement content was reduced by 20% to 40%. Durability related properties include resistance to sulphate attack and acid rain was enhanced greatly. Application of complex mineral superfine powder is an effective way to reduce environment pollution and improve durability of concrete under severe conditions. When KG powder mix with concrete, it will increase the durability of concrete with improved pozzolanic reaction and micro-aggregate filling. As a result of this experimental study, it is noted that the use of 20%, to 40% replacement of KG powder instead of cement, has significant effect on compressive strength of concrete over time and will withstand sulfate attack and acid rain.

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)0.0000.000
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 head0.045
GPT teacher head0.388
Teacher spread0.343 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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Same venueInternational journal of advance research and innovative ideas in educationSame topicConcrete and Cement Materials ResearchFrench-language works237,207