A bench-scale evaluation of different treatment options to produce bio-stable drinking water
Bibliographic record
Abstract
Owing to increasingly stringent water quality regulations and concerns about emerging pathogens, many drinking water utilities are having to modify their treatment lines. Bench-scale evaluation can be a useful means for carrying out a preliminary assessment of treatment modifications under consideration. An example of such an evaluation, performed as an initial screening of treatment options for a big North American utility, is presented here. Four sampling campaigns aimed at investigating at a bench-scale the impact of different treatments (coagulation-flocculation-settling, moderate ozonation, and filtration; high dosage ozonation; chlorination followed by dechlorination) on water quality were performed in this study. Testing involved measurement of water quality parameters (turbidity, dissolved organic carbon, ultraviolet absorbance, specific ultraviolet absorbance) with special attention paid to parameters driving regrowth potential (biodegradable dissolved organic carbon (BDOC) and bacterial abundances). Main results show that ozonation always increases BDOC levels. "Full treatment" (coagulation-flocculation-settling, moderate ozonation, filtration) would not change the regrowth potential of the raw water drastically. A high dosage ozonation ("high O3" scenario) could result in a substantial seasonal increase in BDOC concentrations. It would be logical to follow up this conclusion with a consideration of whether or not the higher bacterial regrowth that would take place in the distribution system, and due to these increases in BDOC concentration, could be controlled by maintaining oxidant residuals during distribution. Key words: drinking water, bench-scale, biological stability, water treatment, biodegradable dissolved organic carbon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".