Assessment of sub-clinical mastitis and its associated risk factors in dairy livestock of Lamjung, Nepal
Bibliographic record
Abstract
INTRODUCTION: Mastitis is one among the top three threats faced by dairy farmers. The study was carried out to assess sub-clinical mastitis, management practices and associated risk factors for mastitis. MATERIALS AND METHODS: A cross sectional study was conducted in Chandreshwor and Archalbot VDCs of Lamjung district taking 63 dairy livestock randomly each from a herd along with questionnaire survey to respective owner. Tem ml of milk sample from each quarter was taken in a sterilized syringe for further laboratory investigation. California Mastitis Test (CMT) was performed at farmer’s shed. Organisms were identified based on colony characteristics, Gram's staining and various biochemical tests. RESULTS: On CMT, subclinical mastitis was 46.1% (n=29) and 30.15% (n=76) on animal and quarter basis respectively, however, culture showed, 28.6% and 24.2%. Streptococcal mastitis was the commonest (11.1%) followed by coliform (9.5%) and staphylococcal (7.9%). Mastitis was highest in left fore quarter (34.92%) followed by left hind (31.76%), right hind (28.57%) and right fore (25.39%). Coliform & Staphylococcal mastitis was highest in left fore and right hind quarter respectively. Most of dairy animals (86%) were on zero grazing, 30% (n=19) of the farmers had forage trees and only 29% (n=18) had known about subclinical mastitis. The average milk production was 3.5 ±1.47 liters. Subclinical mastitis was associated significantly (p<0.01) with livestock yielding more than 3 lt per lactation. CONCLUSIONS: There was high prevalence of subclinical mastitis in dairy livestock at Lamjung due to poor management, unhygienic shed, and little knowledge on subclinical mastitis. DOI: http://dx.doi.org/10.3126/ijim.v2i2.8322 Int J Infect Microbiol 2013;2(2):49-54
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 | 0.000 | 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".