The effect of curing condition and use of additives on compressive strength of lime/pozzolana mortar
Bibliographic record
Abstract
Previous studies have shown that the obsidian pozzolana of the Sabaloka area processes a high pozzolana index. Therefore, it was chosen to study the effect of its curing condition and use of additives on the compressive strength of lime pozzolana mortar. The investigation revealed that the strength of pozzolana mortar is affected positively or increases with the curing condition (humidity, time, and temperature); also the strength increases with the addition of torona (2, 5 and 10%) which the higher the strength reveals the higher the percentage of additives (10%). This is probably due to carbonation. The indirect curing gave higher strength than that of direct curing when we compared the carry fine fractions, respectively for indirect and direct curing. Regarding carbonation, the possibility of absorption of CO2 from the atmosphere is greatly increased by the indirectly cured lime/pozzolana cubes because atmospheric CO2 rates with lime to produce caco3 which is accompanied by a gain in strength. The strength of pozzolana/lime mortar affected by the earing time. Long time of curing (120 days) gave the strength compared to the early strength 7 and 14 days; the same behaviour also for less finer (90 to 63 μ) gave 16 N/cm2 for 120 days as compared to only 11 N/cm2 for 7 days. Key words: Pozzolana, curing condition, humidity, time, tempreture, torona, strength, carbonation.
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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 | 0.001 |
| 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".