First Derivative UV Spectra of Surface Water as a Monitor of Chlorination in Drinking Water Treatment
Bibliographic record
Abstract
Many countries require the presence of free chlorine at about 0.1 mg/l in their drinking water supplies. For various reasons, such as cast-iron pipes or long residence times in the distribution system, free chlorine may decrease below detection limits. In such cases it is important to know whether or not the water was chlorinated or if nonchlorinated water entered the system by accident. Changes in UV spectra of natural organic matter in lakewater were used to assess qualitatively the degree of chlorination in the treatment to produce drinking water. The changes were more obvious in the first derivative spectra. In lakewater, the derivative spectra have a maximum at about 280 nm. This maximum shifts to longer wavelengths by up to 10 nm, decreases, and eventually disappears with an increasing dose of chlorine. The water treatment system was monitored by this technique for over 1 year and changes in the UV spectra of water samples were compared with experimental samples treated with known amounts of chlorine. The changes of the UV spectra with the concentration of added chlorine are presented. On several occasions, water, which received very little or no chlorination, may have entered the drinking water system. The results show that first derivative spectra are potentially a tool to determine, in the absence of residual chlorine, whether or not surface water was chlorinated during the treatment to produce potable water.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".