Cold Water Effects on Enhanced Coagulation of High DOC, Low Turbidity Water
Bibliographic record
Abstract
Abstract Prairie farm reservoirs are usually low in turbidity and contain high concentrations of dissolved organic carbon (DOC). Some are treated with aluminum sulfate in late fall when the water drops below 3°C, often with poor turbidity reduction results. Jar tests using aluminum sulfate were conducted to study the effect of water temperature on enhanced coagulation of a typical high DOC prairie water. Jar tests showed that temperature affects turbidity, particle counts and total residual aluminum but does not affect DOC, UV254 absorbance and colour reduction. Turbidity of the treated water increased consistently as the temperature decreased. Particle counts in the 1 to 10 μm range were reduced by two logs at 20°C but only by one log at 1.5°C indicating inferior oocyst removal at low temperatures. Bentonite addition at 1.5°C did not affect the treated water particle counts but because the bentonite increased the raw water particle count, the reduction of the particles could be increased by one log. Coagulant demand for the water tested without pH adjustment is one to two mg Al per mg of DOC at all temperatures tested. The optimum pH for all temperatures was about 6.0 for DOC removal and about 6.5 for turbidity removal based on a dosage increment of 0.5 pH units. UV254 absorbance is well suited to predict optimum aluminum sulfate dosage for DOC removal.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".