Sustainable technology of trickling biosand filter (TBSF) combined with rock media to reduce organic matters for drinking water
Bibliographic record
Abstract
Access to safe drinking water is still unavailable to many people in developing countries. Biosand filter (BSF) is one of the most promising emerging point of use technologies. A large amount of organic matters is contained in their water source. The purpose of this study is to develop a hybrid BSF system, called as a trickling biosand filter (TBSF), which is combined with rock media as trickling filter to reduce organic matters ranged from CODcr 50 to 150 mg/L in source water. The effects of TBSF and BSF on the factors as CODcr, flux, turbidity, and DO are analyzed. Results showed that the effluent CODcr of TBSF was obtained 2.3–4.2 mg/L during 41 d. However, that of BSF fluctuated within 13.1–28.6 mg/L. DO in standing water of TBSF increased to average 7.8 mg/L while that of BSF decreased to average 1.2 mg/L. DO played an important role to activate microbial activity in trickling filter and to ripen Schmutzdecke layer to decrease constantly turbidity and Escherichia coli (E. coli) in TBSF, though shock loading of organic matters occurred. The turbidity could be removed well if it was originated from organic matters. Removal of E. coli in BSF was fluctuated because of low DO. This could not provide perfect microbial layer on top sand and allow E. coli passing through sand filter, though enough time for ripening.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".