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Record W2171591207 · doi:10.1520/stp11047s

Masonry Wall Materials Prepared by Using Agriculture Waste, Lime, and Burnt Clay

2002· book-chapter· en· W2171591207 on OpenAlexaff
Bernhard Middendorf, J Mickley, Fernando Martirena, R W Day

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLimePozzolanWaste managementBagasseStrawSugar caneHuskCementPozzolanic reactionMaterials scienceGypsumFly ashPulp and paper industryPozzolanic activityEnvironmental sciencePortland cementMetallurgyChemistryEngineering

Abstract

fetched live from OpenAlex

Low cost building materials prepared with ash of burnt agriculture waste and lime represent an alternative binder/construction system. Pure lime and Portland cement (OPC) are energy intensive to manufacture, and are expensive and scarce in developing countries. However, pozzolanic binders prepared by burning agriculture waste can be used as partial or complete substitutes for pure lime or OPC. These agricultural wastes, such as rice husks, wheat straw, sugar cane bagasse and sugar cane straw are widely available in many developing countries. Some types of clay are also pozzolanic after thermal treatment. The reactivity of the ash depends on its composition and on several factors involved in the burning process such as temperature, time, environment and cooling rate as well as chemical activation. This paper presents research focused on building materials produced by using lime combined with a pozzolanic mixture of thermally treated clay and ash from agricultural wastes. The study assesses the accelerating effect of sodium sulfate in strength development of building materials.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.193
Teacher spread0.180 · 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

Citations11
Published2002
Admission routes1
Has abstractyes

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