Slow sand filtration for small water systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For over 150 years, slow sand filters have been an effective means of treating water for control of microbiological contaminants. Slow sand filters do not need constant operator attention, making them an appropriate technology for water systems that are small or that employ part-time operators. During the 1970s through the 1990s, research and field evaluations of slow sand filtration have demonstrated its efficacy for control of microbiological contaminants that were unknown in the 1800s. In addition, pretreatment processes such as roughing filters and pre-ozonation have been developed or adapted for use with slow sand filters, increasing the range of source waters that can be treated and the number of contaminants that can be removed in slow sand filters. Inclusion of a layer of granular-activated carbon in a slow sand filter bed has improved capability for control of synthetic organic chemicals. This paper reviews design concepts and process capabilities for slow sand filters and discusses recent innovations in slow sand filter design that now enable this technology to be applied more widely than would have been appropriate two decades ago. Key words: slow sand filter, design, operation and maintenance, microbiological contaminants, small systems, pretreatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it