Influence of Natural Organic Matter on Bromate Formation During Ozonation of Low-Bromide Drinking Waters: A Multi-Level Assessment of Bromate
Bibliographic record
Abstract
A multi-level approach is used to assess bromate formation. The size, structure and functionality of natural organic matter (NOM) and its role in bromate formation is being investigated via a nationwide survey of ozonation facilities, bench-scale ozonation and simultaneous NOM characterization of source waters, as well as scale-up comparison testing between bench-, pilot-, and full-scale ozone contactors. Initial results indicate that many utilities will be faced with the challenge of optimizing their ozonation process in order to achieve the desired Cryptosporidium inactivation, that may become a consequence of the proposed Stage 2 Disinfectant/Disinfection By-Product (D/DBP) Rule, along with compliance of the existing Stage 1 bromate standard of 10 µg/L. Ongoing work will continue to show that a solid understanding of the character of the NOM will enable utilities to predict how NOM will either inhibit or promote bromate formation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".