Dataset of producing and curing concrete using domestic treated wastewater
Bibliographic record
Abstract
We tested the setting time of cement, slump and compressive and tensile strength of 54 triplicate cubic samples and 9 cylindrical samples of concrete with and without a Super plasticizer admixture. We produced concrete samples made with drinking water and treated domestic wastewater containing 300, 400 kg/m(3) of cement before chlorination and then cured concrete samples made with drinking water and treated wastewater. Second, concrete samples made with 350 kg/m(3) of cement with a Superplasticizer admixture made with drinking water and treated wastewater and then cured with treated wastewater. The compressive strength of all the concrete samples made with treated wastewater had a high coefficient of determination with the control concrete samples. A 28-day tensile strength of all the samples was 96-100% of the tensile strength of the control samples and the setting time was reduced by 30 min which was consistent with a ASTMC191 standard. All samples produced and cured with treated waste water did not have a significant effect on water absorption, slump and surface electrical resistivity tests. However, compressive strength at 21 days of concrete samples using 300 kg/m(3) of cement in rapid freezing and thawing conditions was about 11% lower than concrete samples made with drinking water.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".