Development of one part, Self-Cured, Fine Soil/ Quartz Stone Powder Based Geopolymer Mortar
Bibliographic record
Abstract
One of the critical challenges in the practical application of geopolymers lies in the necessity of heat treatment to enhance their properties.Consequently, the development of self-curing geopolymers has emerged as a focal area of research.In this study, fine soil was utilized as the primary raw material.Two series of mixes were prepared: in the first series, without (%0) Ordinary Portland Cement (OPC) replacement, fine soil was replaced by quartz stone powder at varying proportions (0%, 20%, 40%, 60%, 80%, and 100%).In the second series, with a 10% OPC replacement, fine soil was replaced by quartz stone powder in increments of 0%, 15%, 35%, 55%, 75%, and 90%.Key properties such as compressive strength, water absorption, and sorptivity were investigated.The results revealed that the mix with 10% OPC replacement achieved better the geopolymer properties.Furthermore, in both series, increasing the quartz stone powder content consistently improved the mortar performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".