Risk factors associated with the incidence rate of clinical mastitis in smallholder dairy cows in the Dar es Salaam region of Tanzania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smallholder dairy herds around the Dar es Salaam region of Tanzania supply 86% of raw milk consumed by the city dwellers. Previous studies have indicated that clinical mastitis is an important disease affecting smallholder dairy cows and an 18-month questionnaire-based longitudinal field-study was conducted between July 2003 and March 2005 to elucidate risk factors associated with the incidence. A total of 6057 quarter-level observations from 317 lactating cows on 87 randomly selected smallholder dairy herds were analysed at the quarter and cow level using logistic and Poisson regression models, respectively. At the quarter level, the average incidence rate of clinical mastitis was 38.4 cases per 100 quarter-years at risk whereas at the cow level the incidence rate was 43.3 cases per 100 cow-years at risk. The incidence was significantly (P< or =0.001) associated with cow factors (body condition score, parity, stage of lactation, and udder consistency), housing (floor type) conditions and milking (cow and udder preparation) practices. It was concluded that the extrapolation of the classic ten-point mastitis control plan into smallholder dairy herds should be undertaken cautiously. An integrated approach to dairy extension should focus more on the creation of mastitis awareness among smallholder producers and on the improvement of animal nutrition and reproduction indices-factors that may also have a direct impact on milk yield.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it