PREVALENCE OF SUBCLINICAL MASTITIS IN A DAIRY HERD IN BENI-SUEF GOVERNORATE
Bibliographic record
Abstract
A total of 115 dairy cows were screened by California mastitis test (CMT) to estimate the prevalence of subclinical mastitis in a dairy herd in Beni-Suef Governorate, as well as Somatic cell count ( SCC) of 28 bulk tank milk samples were estimated using De-Laval cell counter. Mean bulk tank SCC (BTSCC) was 9.5 x 105 + 7.5 x 104 with the highest frequency of distribution (64.3%) lies within the range of 5x 105 to 1 x 106 . The prevalence of subclinical mastitis was 15.2 % on an udder quarter basis and 39.1% on a cow basis. The organisms that were most frequently isolated were E.coli (35.4%), Str.bovis (21.5%), Str.agalactiae (10.8%), Coagulase negative Staphylococci (CNS) (7.7%), S.aureus (6.2%), Str.dysgalactiae (3.1%) and Str.faecalis (3.1%) for single infection while for double infection were Str.bovis with S.aureus (6.2%), Str.bovis with E.coli (3.1%), Str.agalactiae with S.aureus (1.5%) and Str.bovis with CNS (1.4%). In conclusion subclinical mastitis is a serious problem in dairy industry and its early detection is the corner stone in its control.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".