Effect of Sand Bed Depth and Medium Age on <i>Escherichia coli</i> and Turbidity Removal in Biosand Filters
Bibliographic record
Abstract
The main objective of this study was to build several full-scale biosand filters (BSFs) and assess the long-term (9 month) efficacy for particulate and Escherichia coli removal under simulated real-world usage. Four replicates of three different filter designs were built: the traditional concrete BSF and two scaled-down versions that use a 5 or 2 gal bucket as the casing material. The smaller sand bed depths in the bucket-sized filters did not impact filter performance with respect to (i) turbidity and E. coli removal or (ii) effluent levels of turbidity and E. coli. All filters produced effluents with a mean turbidity of <0.6 nephelometric turbidity unit. In addition, 78, 74, and 72% of effluent samples for the concrete, 5 gal, and 2 gal filters, respectively, had E. coli concentrations of <1 colony-forming unit/100 mL. The bucket-sized filters were found to be a potential alternative to the concrete BSFs for the removal of E. coli and turbidity from drinking water. Because smaller BSFs must be filled more frequently than larger BSFs to produce comparable water volumes, the effect of shorter pause periods on BSF performance should be investigated.
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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 | 0.001 | 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".