Knowledge Index for Measuring Knowledge and Adopting Scientific Methods in Treatment of Reproductive Problems of Dairy Animals
Bibliographic record
Abstract
Reproductive problems among dairy animals are one of the major causes of loss in dairy sector. These problems can be tackled by imparting appropriate knowledge to the livestock owners. An attempt was made to measure the knowledge of livestock owners by developing a knowledge test on reproductive problems of dairy animals. The study was undertaken in Karnal district of Haryana state, India. Data were solicited from 300 livestock farmers who had at least one milch animal at the time of investigation. In addition to developing schedules for socio-economic variables, a knowledge test was also developed for measuring knowledge construct. Data were solicited on scientific treatment of affected dairy animals and 59.54% knowledge was observed on reproductive traits. Study indicates that majority of livestock farmers adopted scientific methods for treating their animals. Respondents’ age, extension contact and milk production were positively and significantly correlated with knowledge. Therefore, imparting quality practical training and periodical assessment of performance of lay inseminators for improving their skills and knowledge regarding estrus detection and insemination needs to be emphasized. Extension machinery has to be an ideal bridge between research/development institutions and dairy farmers for their catalytic effect (Meena & Malik, 2009). Extensive awareness programs are needed for inculcating scientific outlook among livestock farmers on these complex problems. Easy accessibility of veterinary hospital at village level can reduce the adoption of indigenous technical knowledge in treatment of these complex problems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".