Bibliographic record
Abstract
Abstract Contamination in water distribution network (DN) can be catastrophic if free residual disinfection is not present. To ensure drinking water quality in a DN, ensuring detectable levels of free residual chlorine is a common practice in most municipalities. Higher chlorine doses can be applied in this respect, but this can produce detrimental disinfection by‐products (DBPs) such as trihalomethane and Haloacetic acids, and chlorine‐related taste and odor complaints. Booster chlorination can be a possible solution in this regard where additional chlorine dosages are applied where free residual chlorine concentration is less or zero. However, that requires optimization studies to balance various issues related to over chlorination including higher cost related to installation and operation, higher DBPs, and taste and odor complaints. These optimization studies can not only help us in selecting dosages but also can help us in various other aspects including locating booster stations, deciding number of stations, and pumping scheduling. Unlike other optimization studies, booster chlorination‐related optimization studies also require appropriate variables, kinetics, objective functions, constraints, mathematical formulation, algorithm selection, and proper optimization operation. This article will highlight details of all these steps specific to booster chlorination.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".