Monitoring natural organic matter in drinking water treatment with photoelectrochemical oxygen demand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Conventional metrics such as total organic carbon (TOC) and ultraviolet absorbance at 254 nm (UV 254 ) 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 (R 2 = 0.67) and UV 254 (R 2 = 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 (R 2 < 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it