Maintenance strategies for on-site water disinfection by ultraviolet lamps on dairy farms
Bibliographic record
Abstract
Bacteria have been detected in many water supplies on dairy farms, and the water used to wash milking equipment should be free of bacteria. This study evaluated two ultraviolet (UV) technologies, with and without automatic sleeve cleaning device, as on-farm water disinfection system. No fouling was observed during 6 months of continuous operation of the self-cleaning UV system. The dose provided by a non self-cleaning UV lamp decreased by 41% and 96% after 30 and 60 days of continuous operation, respectively, at the maximum recommended water hardness and iron levels. However, when the lamp was operated 2 hours, twice daily, as on dairy farms disinfecting water solely to wash milking equipment, the dose decreased by 9% and 50% after 30 and 60 days, respectively. A UV system with self-cleaning capability is thus recommended for most farms, or monthly manual cleanings will be required to limit fouling and ensure water disinfection. Both UV technologies were efficient in disinfecting water containing high pathogen concentrations. A dose of 136 mJ/cm2 completely deactivated 1090 and 595 CFU/100 mL of total and fecal coliforms, respectively. Under proper management, the use of UV lamps could thus provide bacteria-free water to wash milking equipment on dairy farms.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".