Development of Ceramic Water Filter Clay Selection Criteria
Bibliographic record
Abstract
Ceramic water filters (CWFs) are point-of-use drinking water treatment systems that are manufactured and used in under-served communities around the world. The clayey material (CM) used to manufacture CWFs is a locally sourced mixture of clay, sand, slit and amorphous material (usually dug near the CWF factory). CM varies in composition and purity depending on the geographical location and geological setting. In this study, a set of 13 CM samples collected from around the world were analyzed using grain size analysis, as well as liquid and plastic limit tests. Mineralogical composition was determined using X-ray diffraction. A selection of three CM samples (Guatemala, Canada, and Guinea Bissau) with a range of compositions were used to study biofilm growth on CM before and after firing. Biofilm coverage was studied on CM (before firing) and CWF material (after firing) using Pseudomonas fluorescens Migula. The average biofilm coverages for Guatemala, Canada, and Guinea Bissau CM were 20.03 ± 2.80%, 19.28 ± 0.91%, and 9.88 ± 4.02%, respectively. The average biofilm formation coverages for Guatemala, Canada, and Guinea Bissau CWF were 13.08 ± 1.74%, 10.36 ± 3.41%, and 8.66 ± 0.13%, respectively. The results presented here suggest that CM can be manipulated to manufacture better performing CWFs by engineering the soil characteristics, such as grain size, liquid and plastic limits, and mineralogy. This could improve the durability and biofilm resistance of CWFs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".