An approach for determining the most critical among a suite of chemical contaminants at a drinking water intake
Bibliographic record
Abstract
An integrated approach for the identification and assessment of the most critical chemical contaminant(s) at a drinking water intake has been developed. It involves the determination of a threshold or critical raw water concentration (CRWC) for target contaminants using the observed overall removal efficiency of a specific water treatment plant (WTP) and regulated drinking water concentrations for the target contaminants. The exceedance probability relative to the CRWC based on historical raw water quality monitoring data is then calculated. Finally, the integration of the raw water quality data and the overall efficiency of a particular WTP sequence allows for identification of the most critical contaminant(s) as well as an advance indication of which contaminants are most likely to challenge a plant. The proactive nature of this approach gives a utility the impetus and time to assess current treatment processes and potential alternatives. In addition, it was found that three- or four-parameter theoretical distributions are more appropriate than two-parameter probability distributions for the fitting of raw water quality data. This study reveals that the reliance on raw and/or treated water contaminant concentrations in isolation or on theoretical removals through treatment processes can, in some circumstances, be misguided.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 teacher head, 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".