Bibliographic record
Abstract
Scale-up feasibility of the graphitized sand filter (GS1) for Microcystin-LR (MC-LR) removal and its impact on other water pollutants (WPs) was assessed through a mass-balance study, using a laboratory-based drinking water treatment plant (DWTP) micromodel named: SAP-1©. The treatment system comprised: raw water tank, pre-oxidation tank (oxidant: potassium permanganate), followed by a coagulation/flocculation tank (alum supplemented), sedimentation tank, filtration module and finally disinfection tank (dosed with hypochlorite solution). Two filter modules (FMs) were studied: a) FM1: graphitized-sand media + sand media = ½ GS1 + ½ sand and b) FM2: ½ sand + ½ sand. The MC-LR removal study (initial concentration: 50 μg/L) was performed for two varieties of MC-LR source: a) commercial MC-LR, and b) algal-biomass released MC-LR. Along with MC-LR, other WPs were also evaluated including metal ions (Fe<sup>2+</sup> and Cu<sup>2+</sup>), total coliform, turbidity, ammonia-N and dissolved organic carbon. The removal efficiency of these WPs was determined for each treatment unit (as it passed). FM1 was able to reduce the inflow residual of MC-LR (coming from the preceding unit: sedimentation unit) from 12.1 μg/L and 25.4 μg/L (for commercial and algal-cell MC-LR source, respectively) to <0.61 μg/L and hence successfully complying the WHO guidelines (<1 μg/L). The protein phosphatase 1A (PP1A) toxicity assay confirmed a much safer and more toxic-free filtrate (by 40%-50%) for FM1 as compared to the filtrate obtained from FM2. The techno-economic evaluation showed that for an annual household filter application, 160 CAD needs to be spent on one GS1-based filter unit as compared to over 6000 CAD (equivalent price) for the conventional sand-based filter to provide MC-LR-free water. The present study demonstrates the feasibility of the utilization of these units in household filtration systems.
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 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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".