Characterisation and pore structure analysis of mortar incorporating valorised rice husk ash
Bibliographic record
Abstract
Physical, chemical, and mineralogical characterisation of as-received rice husk ash (RHA) samples sourced from four rice-growing regions (North, South, East, and West) of India is presented. Valorised RHA was obtained through controlled combustion at two temperature ranges (600–700 °C and 650–700 °C) of husks from the North in an industry set up. Valorisation efficacy has been tested through comparative characterisation of the valorised RHAs with the as-received RHAs from the four regions. Blending of 15% valorised RHA (in the beneficiated state) by weight of cement had no adverse effect on compressive strength even though water–binder ratio of the blended mortar had to be increased by 14% to achieve flow of the control cement mortar. Compared with the control cement mortar, porosity of the mortar blended with the beneficiated RHA measured using mercury intrusion porosimetry (MIP) increased by up to 10% primarily due to an increase in the number of large mesopores (0.01–0.05 µm).
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".