Slow sand filtration pre-treatment with alum is efficient, but is it effective?
Bibliographic record
Abstract
Enhancing the performance of slow sand filter pre-treatment with alum (i.e. direct or contact filtration) has been proposed as an alternative to overcome limitations of conventional gravel (roughing) pre-filtration. Experimental results revealed high turbidity reduction efficiencies in alum-dosed pre-filtration. However, due to the alum coagulation, the nature of the particulates in the pre-treatment effluent changed and had a potential to shorten the downstream slow sand filter run lengths by approximately 50% under the conditions tested. Hence, depending on the effluent turbidity levels the effectiveness of the alum-dosed pre-treatment could be compromised, despite its high efficiency. Relatively low turbidity levels (<2 NTU) were needed to minimise excessive headloss development in alum-dosed filters. However, the necessary resources to achieve such process control may not be available in developing country contexts. Furthermore, full-scale field experiences indicated the limited effectiveness of alum dosing prior to slow sand filters and the difficulties in maintaining an adequate chemical dosing in under-resourced settings.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".