Trihalomethane formation potential in selected drinking waters of Lebanon
Bibliographic record
Abstract
Laboratory-scale simulated distribution system trihalomethane (SDS-THM) tests were conducted on selected public drinking water sources to predict as well as evaluate trihalomethane formations under controlled laboratory conditions. Varying concentrations of SDS-total trihalomethane at any time (SDS-TTHMT) (2.68–85.78 μg/L) were detected in the two simulated water sources. Evaluating the impact of each test variable on attained SDS-TTHMT levels revealed that for both water sources, the majority of samples exhibited higher SDS-TTHMT levels in the presence of higher chlorine doses. For the two sources, 85 to 87.5 percent of the sample pairs exhibited higher SDS-TTHMT levels at higher incubation temperatures. Incubation periods at which maximum THM levels were attained varied with water source type as well as pH values. All comparable samples exhibited SDS-TTHMT values higher (1.41 and 7.07 folds) than actual surveyed TTHM levels. Statistically, SDST-TTHM showed significant correlations with applied chlorine dose at the 0.05 level, and with TOC, bromides, contact time, and temperature at the 0.01 level. The predictive model, formulated using multiple regression approaches, exhibiting the highest coefficients of determination was logarithmic for the laboratory simulated THM database (r2=0.70; p<0.001) with a very high significance level (<0.01 level).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".