Real-Time Estimation of Disinfection By-Products through Differential UV Absorbance
Bibliographic record
Abstract
Efforts to control of unwanted disinfection by-products (DBPs) can be hindered by the relatively low-frequency data that is generated from DBP monitoring for regulatory purposes. Differential UV absorbance (DUVA) has the potential to overcome some of these difficulties as a relatively simple and low-cost technique for the estimation of regulated DBPs (i.e., trihalomethanes, THMs, and haloacetic acids, HAAs). DUVA is based on the principle that differences in UV absorbance at specific wavelengths before and after chlorination can be correlated to DBP occurrence. This project sought to assess a lab-based protocol for the determination of a site-specific linear calibration curve for in situ real-time estimations of DBPs from DUVA measurement in water treatment plants (WTPs). A field spectrophotometer probe capable of high frequency scans within the UV light wavelength spectra with light paths of 100 mm was used. Models were developed to estimate DBP levels, at a scale representative of DUVA and DBP levels that actually occur in the WTP under study. Results showed that level of uncertainty on DBP levels estimation is relatively low (23% for HAAs and 32% for THMs). Although DBP estimations through DUVA are not of regulatory value, they can be used for real-time estimation of DBP levels to better inform operational decision making in water treatment plants and management of DBPs in distribution systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".