Correlation Estimates between Carcass Traits of Nili Ravi and Kundhi Buffalo
Bibliographic record
Abstract
Present study was designed to estimates the correlation between carcass traits of Nili Ravi and Kundhi buffalo. The data for carcass traits of Nili Ravi and Kundhi buffalo was collected from Seven Star International Meat Processing Company Dhabeji at Thatta. In current study the data of total 100 animals of Kundhi and Nili Ravi breed were selected and divided into A, B, C and D group. In group A and C there were Kundhi and Nili Ravi male whereas, B and D females of both breeds respectively. The data including live body weight, carcass weight, dressing percentage and boneless weight of both breeds Kundhi and Nili Ravi were collected for the estimation of correlation.The results for correlation estimates of different carcass traits indicated that the correlation estimation were found positive and high among Nili Rave breed as compared to Kundhi breed, which shows that an increase in one carcass trait would increase the other carcass traits. It was concluded that Nili Ravi carcass traits are better expressed and produces more beef than Kundhi, while Kundhi male is better in beef production than the Nili Ravi female whereas Kundhi female produces low carcass yield.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".