Disinfection by-products in drinking water – a case study on Calgary, Alberta, Canada
Bibliographic record
Abstract
Disinfection by-products (DBPs) have been monitored in Calgary's drinking water for approximately 15 years. The variability of the DBPs has typically exhibited similar patterns over the period of monitoring. Due to the nature of the surface waters supplying the water treatment plants, the level of DBPs was largely influenced by surface runoff events where the level of natural organic matter (NOM) increased, which was characterized by a relatively high total organic carbon (TOC) content. Principal component analysis (PCA) was utilized for this study to quickly identify the key underlying correlations present within the very large, complex multivariate data matrix. Apart from TOC, chlorine demand, chlorine residual and temperature were observed to correlate with the formation of DBPs in the finished drinking water. In addition to TOC, PCA also indicates that pH and temperature in the distribution system could have an influence on the variability of DBPs in Calgary's drinking water. It was apparent that upgrades to the water treatment systems in Calgary have resulted in an improved removal of DBP precursors such as NOM prior to chlorination, which is a key factor in reducing the DBP levels in the drinking water, thereby providing an enhanced level of public health protection.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".