How do different species of Disinfection By-Products compare to water quality guidelines?
Bibliographic record
Abstract
Water treatment is a critical component to ensure safe drinking water provisions for Canadians. In particular, chlorine is commonly used to disinfect drinking water supplies. However, chlorine can react with dissolved organic carbon (DOC) to form carcinogenic disinfection by-products (DBPs). Although thousands of different types of DBPs can form (ranging in abundance and toxicity) it is too difficult to measure, let alone regulate, every single compound. For that reason, drinking water guidelines generally use the total concentration of two groups (trihalomethanes and haloacetic acids), encompassing various combinations of carbon, chlorine, and bromine (termed DBP species). However, these guidelines are chemically confusing (adding up different concentrations on a weight basis rather than a molar basis) and group together a number of different species with different levels of health concerns. This poster will start a conversation about DBP water quality guidelines. First, we provide a tool to easily calculate and convert DBP guideline values to various DBP molar concentrations. Second, we compare DBP data collected from various lakes (covering 15º of latitude, from the IISD-Experimental Lakes Area in Ontario, to the Northwest Territories) to examine how differing amounts and mixtures of DOC result in changes in the relative formation of various DBP species. Finally, we discuss how different species may vary in toxicity. These components all help to continue a conversation that discusses DBP formation, risk, and water quality guidelines.
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 | 0.028 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".