Prevalence of Mastitis and Effectiveness of Mastitis Control in Dairy Cattle in Mathira Constituency, Nyeri County, Kenya.
Bibliographic record
Abstract
This study assessed prevalence of mastitis and effectiveness of mastitis control in dairy cattle in Mathira constituency. Data regarding occurrence of mastitis, farmers' current practices in mastitis control, and administering a questionnaire to 76 smallholder farmers collected their knowledge about dairy cow mastitis. Quarter milk samples were collected from randomly selected lactating cows and screened for mastitis using the California Mastitis Test. Milk samples that tested positive on screening were cultured for isolation of pathogens. A total of 202 lactating cows were sampled. The prevalence of mastitis at quarter level was 87.4% (n=808) with contagious bacteria pathogens being responsible for 52.2% (n=508) of all the isolates recovered. Most of these contagious isolates were coagulase positive Staphylococcus aureus, (98.5%), the rest being Streptococcus agalactiae. Normal teat flora, opportunist and environmental pathogens were responsible for 32.7%, 7.3% and 4.5% of all the isolates respectively. Stimulation (93.4%), prompt and adequate treatment of clinical cases(77.6%) and sanitation(69.7%) were the only control measures noted to have been embraced adequately. Sanitation, use of individual towel, and stimulation were ranked as the most effective having been scored by 39, 24 and 23 respondents respectively. It is concluded that concerted effort by all stakeholders is paramount if the war against mastitis in Mathira Constituency is to be won.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".