The Effect of Using Different Detergents in Cleaning Cows' Udders on The Microbial Content of Produced Milk
Bibliographic record
Abstract
This study investigated the effect of different detergents used to clean cows' udders on the microbial content of the produced milk using twenty cows in Ajloun, a northern city in Jordan. The milking process was repeated from same cows on three successive days. On day 1, we milked the cows after cleaning their udders using water only. This was repeated on the two successive days. Thereafter, the cows were milked after cleaning their udders by a different detergent each day. The process was also repeated for three successive days for each detergent. Microbial Analysis was carried out on the collected milk samples. The results indicated that cleaning cows' udders before milking has improved the hygiene conditions and reduced the total bacterial count, total coliform, staphylococci and enterococci spp counts and the values of yeast and molds. Different detergents had different effects on the microbial counts. Finally, the effectiveness of the detergent differed according to its brand. Our findings are important to public health because milk has been a traditional food and ironically a very potent carrier of gastrointestinal infections, if contaminated.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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".