Laboratory Efficacy and Disinfection by-Product Formation of a Coagulant/Disinfectant Tablet for Point-of-Use Water Treatment
Bibliographic record
Abstract
Coagulant/disinfection products (CDPs) are a point-of-use (POU) water treatment technique that can improve microbial quality, reduce turbidity, and produce a free chlorine residual (FCR), serving as a potentially effective option for decentralized water treatment in a variety of contexts, including humanitarian emergencies. A novel CDP with a sodium dichloroisocyanurate-based disinfectant was evaluated with regard to its laboratory water treatment efficacy and generation of disinfection byproducts (DBPs). The CDP water treatment performance was assessed relative to bacteriological (E. coli) humanitarian water quality objectives, World Health Organization recommendations for evaluating POU water treatment options, and available DBP regulations and guidelines. At least 4 log10 E. coli reductions, for a “highly protective” status with regard to bacterial reductions, were attained in the tested conditions. Treated waters were consistently below 10 MPN/100 mL with regard to E. coli concentrations, with the majority of samples showing no detectable E. coli. For most conditions, target FCR values were not attained. Treated water turbidity levels were mostly between 5 NTU and 10 NTU. DBP levels were below the regulatory and health-based targets for both families of DBPs studied. This study has served to identify the performance envelopes of the CDP tested under challenging conditions.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".