젖소 준임상형 유방염 우유에서의 원인균 분석
Bibliographic record
Abstract
The occurrence of mastitis in diary cattle has been caused by genetic, physiological, managemental and environment factors accounted for the highest percentage of worldwide disease in dairy cattle. The purpose of this study was to analyze the occurrences and causative bacteria of subclinical mastitis in milking cows and also examine the distribution of bacteria in milk by isolating and identifying bacteria both in whole milk and quarter milk. 31.4% of the milking cows suffered subclinical mastitis, and 9.5% had it in terms of quarter milk. According to the results of analyzing bacteria in quarter milk of which somatic cell count (SCC) was over 500 thousand, 15 kinds of bacteria were isolated, and among them, Pantoea spp. formed the biggest part as 15.8%. From whole milk, 37 kinds of bacteria were identified, and among them, Klebsiella oxytoca showed the highest identification rate as 30.1%. According to the results of bacteria analyzed from the quarter milk of entire milking cows, 52 kinds of bacteria were identified. Among them, 17 kinds of Staphylococci were isolated, and CNS (Coagulase-Negative Staphylococci) formed a large part as 44.9%. The findings of this study showed that various kinds of bacteria were isolated from cows having subclinical mastitis; therefore, when managing specifications about milking or such, dairy farm will have to take proper action like performing sanitary control or counting somatic cells regularly in order to do their best for reducing mastitis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".