Microbial and Physico-Chemical Characteristics of Surface Water Sources Used on Dairy Farms in Ontario
Bibliographic record
Abstract
Abstract Six surface water sources used to wash milking equipment and provide drinking water to the animals on dairy farms in Eastern Ontario were characterized for microbial and physico-chemical characteristics over a 16 month period. The water sources were selected because they had a history of microbial contamination and presented a wide selection of physico-chemical characteristics. Results are discussed with respect to available on-site disinfection technologies. Total and fecal coliforms were detected in over 90% of all water samples, Escherichia coli and Enterococcus spp. in 77%, Clostridium perfringens and Yersinia enterocolitica in about 50%, Campylobacter spp. in 36%, and Salmonella in 25%. For all pathogens, counts were highly variable, and maximum values were 1 to 2 orders of magnitude higher than the medians. On-site disinfection systems will have to be designed to deactivate the highest count of pathogens, thereby providing a conservative safety margin for most of the year. Disinfection technologies will also have to be selected based on physico-chemical characteristics of the water sources, especially the level of hardness, turbidity and UV transmittance which can adversely affect their efficiency. On some farms, a pretreatment such as coagulation flocculation will be necessary to make the surface water suitable for low-cost disinfection technologies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".