Performances of cattle and goats in some selected areas of Gaibandha district in Bangladesh
Bibliographic record
Abstract
The survey was conducted to represent the livestock production scenario and to know the performances of cattle and goats in few selected rural areas of Gaibandha district. The data on productive and reproductive performances of cattle and goats were collected from 102 household within four selected village under Gobindaganj Upazila of Gaibandha district with a pretested survey questionnaire. The collected data were compiled, tabulated and analyzed by student t test. In the study area, about 59% were indigenous cattle and 41% were crossbred cattle. A total of 113 goats were found in the study area of which 82% belongs to Black Bengal goat and the remaining 18% belongs Jamunapari goat. All the livestock (100%) were managed intensively during the spring and rainy seasons. Similarly, almost all livestock (100%) are confined in shed at night, and 45.9% and 54.09% of the livestock population are confined in shed and paddock at day time, respectively. Artificial insemination (AI) is the preferred breeding method for cattle, whereas goats primarily rely on natural mating. Milk yield and lactation period of cow, and mature body weight of both male and female were significantly (P<0.05) higher in crossbred than indigenous cattle. Similarly, age at first kidding, lactation period and kidding interval of does, and mature body weight of buck and does were significantly higher in Jamunapari goats than Black Bengal goats. However, the conception rate and number of kids per kidding were higher in Black Bengal goats than Jamunapari goats. In conclusion, from our results, crossbred cattle and Jamunapari goats are performing better in rural conditions, whereas Black Bengal goats are efficient in producing more offspring at a given time. Bangladesh Journal of Animal Science 52 (3): 78-84.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".