Biological aspects of slow sand filtration: past, present and future
Bibliographic record
Abstract
For over 200 years, slow sand filtration (SSF) has been an effective means of treating water for the control of microbiological contaminants in both small and large community water supplies. However, such systems lost popularity to rapid sand filters mainly due to smaller land requirements and less sensitivity to water quality variations. SSF is still a particularly attractive process because its operation does not require chemicals or electricity. It can achieve a high level of treatment, which is mainly attributed to naturally-occurring, biochemical processes in the filter. Several microbiologically-mediated purification mechanisms (e.g. predation, scavenging, adsorption and bio-oxidation) have been hypothesised or assumed to occur in the biofilm that forms in the filter but these have not yet been comprehensively verified. Thus, SSFs are operated as ‘black boxes’ and knowledge gaps pertaining to the underlying ecology and ecophysiology limit the design and optimisation of the technology. The objective of this review is to outline the biological aspects of SSF in to the context of recent developments in molecular microbial ecology.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".