Effect of water-to-cement ratio and curing method on the strength, shrinkage and slump of the biosand filter concrete body
Bibliographic record
Abstract
The biosand filter is a household-level water treatment technology used globally in low-resource settings. As of December 2016, over 900,000 biosand filters had been implemented in 60 countries around the world. Local, decentralized production is one of the main advantages of this technology, but it also creates challenges, especially in regards to quality control. Using the current recommended proportions for the biosand filter concrete mix, slump was measured at water-to-cement ratios of 0.51, 0.64 and 0.76, with two replicates for each level. Twenty-eight-day strength was tested on four replicate cylinders, each at water-to-cement ratios of 0.51, 0.59, 0.67 and 0.76. Wet curing and dry curing were compared for 28-day strength and for their effect on shrinkage. Maximum strength occurred at water-to-cement ratios of 0.51-0.59, equivalent to 8-9.3 L water for a full-scale filter assuming saturated media, corresponding to a slump class of S1 (10-40 mm). Wet curing significantly improved strength of the concrete mix and reduced shrinkage. Quality control measures such as the slump test can significantly improve the quality within decentralized production of biosand filters, despite localized differences in production conditions.
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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) | 0.000 | 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) | 0.002 | 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".