Trihalomethane formation potential of DOC fractions isolated from two Canadian Prairie surface water sources
Bibliographic record
Abstract
This study evaluated the dissolved organic carbon (DOC) composition of two surface waters in Manitoba, Canada, the Red and Assiniboine Rivers, to identify DOC fractions with the greatest potential to form trihalomethanes (THMs). The DOC was fractionated into six fractions based on hydrophobicity and acid base functionality: hydrophobic acid (HPOA), hydrophobic base (HPOB), hydrophobic neutral (HPON), hydrophilic acid (HPIA), hydrophilic base (HPIB), and hydrophilic neutral (HPIN). The trihalomethane formation potential (THMFP) of the isolated fractions was measured. Fractions collected from the Red River showed significant variation in the specific THMFP, normalized to DOC concentration, ranging from 23 to 1,372 μg THM/mg DOC, while the Assiniboine River THMFP ranged from 11 to 575 μg THM/mg DOC. Although the DOC composition of the two rivers was similar, the THMFP for the fractions differed significantly. The Red River HPOB and HPIB fractions had the highest normalized THMFP of 1,002 μg THM/mg DOC and 1,372 μg THM/mg DOC, respectively, while the HPOA fraction showed the lowest with 23 μg THM/mg DOC. Comparatively, the HPOA fraction from the Assiniboine River was found to have a higher specific THMFP of 478 μg THM/mg DOC. These findings suggest that THM formation is dependent on unique organic composition of the local water and that caution should be taken when estimating THMFP from DOC fractions isolated from different water sources.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".