Coagulation–flocculation pre-treatment of surface water used on dairy farms and evaluation of bacterial viability and gene transfer in treatment sludge
Bibliographic record
Abstract
On many dairy farms, the water used to wash milking equipment is contaminated with bacteria and has to be disinfected. Often, the water requires a coagulation–flocculation (CF) pre-treatment to reduce turbidity and remove dissolved organics prior to disinfection. This paper examines the effect of temperature and water characteristics on the efficiency of an on-farm CF treatment using polyaluminum chloride (PACl) as coagulant. Since the CF process concentrates suspended solids and bacteria in a sludge that will be land-applied, Escherichia coli survival and gene transfer occurrences in the sludge were also determined. Coagulant dose was highly correlated to water UVA254 nm, but not turbidity. For water with variable UVA254 nm, exceeding 0.85 cm−1, the coagulant dose could be adjusted using a simple online UVA254 nm sensor, while settling time should be increased when water temperature drops below 10 °C. E. coli survived a 2-h PACl exposure at a dose of 0.05 mL ClearPAC/L. There was no difference in conjugative transfer of a multi-drug resistance conferring plasmid in water without PACl and in the PACl-derived sludge over a 2-day period. However, since bacteria remained viable in sludge and genetic conjugation may occur, sludge residues should be stored in the manure tank prior to land application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".