Monitoring natural organic matter in drinking water treatment with photoelectrochemical oxygen demand
Bibliographic record
Abstract
Abstract Conventional metrics such as total organic carbon (TOC) and ultraviolet absorbance at 254 nm (UV254) may oversee aspects of natural organic matter (NOM) reactivity in drinking water treatment. The novel photoelectrochemical oxygen demand (peCOD) analyzer indirectly measures the oxygen consumed during NOM oxidation with photo‐ and electrochemical methods, quantifying NOM reactivity. peCOD was valuable for tracking NOM degradation in nine drinking water treatment facilities, particularly in processes where conventional metrics failed to capture changes in NOM from partial oxidation (e.g., biofiltration and oxidation). However, peCOD exhibited moderate correlations with TOC (R2 = 0.67) and UV254 (R2 = 0.48), indicating the need for its concurrent use with conventional methods. While peCOD was not a significant predictor of disinfection by‐product formation potential (R2 < 0.20), its inclusion alongside standard NOM metrics improved the performance of multivariable regression models. Thus, peCOD provided a rapid, standardized, operator‐friendly, environmentally conscious, concentration‐based approach for evaluating NOM characteristics in drinking water samples.
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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 | 0.001 |
| 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) | 0.000 | 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 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".