Strength and Performance of Straw Ash Cement Mortar
Bibliographic record
Abstract
This paper mainly explores the strength and performance of rice straw ash cement mortar. Firstly, the straw ash was obtained through high-temperature calcination, and the ash formation rate was measured. Then, the straw ash was rinsed with clean water and mixed into cement mortar test pieces. According to the mass ratio of cement in the test pieces, the proportion of straw ash were determined as 5 %, 10 %, 15 % and 20 %, respectively. Next, the following properties of each test piece were measured, including water absorption rate, 3d and 28d flexural strength and compressive strength. On this basis, the flexural strength and compressive strength were fitted and correlated with the proportion of straw ash. The fitting formulas and correlation functions proposed in this paper lay a theoretical basis for the engineering application of straw ash cement mortar.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".