Evaluation of approaches for consumers to eliminate chlorine off-flavors from drinking water at point-of-use
Bibliographic record
Abstract
Chlorine off-flavors of tap water have caused dissatisfaction and distrust from some consumers, placing pressure on operators concerning water disinfection. Evaluating practical approaches for eliminating chlorinous off-flavors by consumers at point-of-use while avoiding production of toxic byproducts is a practical concern. Three recognized dechlorination methods: ultraviolet (UV) irradiation, ascorbic acid (AA) and hydrogen peroxide (HP), were evaluated for chlorinated and chloraminated waters. AA is the most efficient for removing free chlorine and chloramine from water samples. Three new chlorine-containing compounds were detected and identified from the reaction between AA and chlorine. High doses of UV irradiation at 254 nm virtually eliminated chlorine. HP could effectively remove free chlorine but was not effective for chloramine elimination. AA shows promise as a practical household dechlorination agent. However, to assure consumers about drinking water safety, further investigation is needed to evaluate any potential toxicity concerns for reaction products in treated water.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".