Direct – Maternal Genetic Parameters Estimated for Real-Time Ultrasound Scan Measures of Eye Muscle Area, Rib Fat Depth, and their Correlation with Body Weight in Male Philippine Riverine Buffaloes
Bibliographic record
Abstract
Background: Ultrasound scanning is a non-invasive technique for evaluating animals based on carcass yield and meat quality traits. It has been incorporated into the beef cattle breeding program. However, little has been done in buffaloes. Thus, this study was conducted to estimate the genetic parameters for growth traits- body weights (BW) and a real-time ultrasound scan of eye muscle area (EMA) and rib fat (RF) to determine their suitability as a tool for selection. Methods: Four hundred thirty-eight (438) male Philippine riverine buffaloes with 1,535 BW records and 417 real-time ultrasound scans of EMA and RF scans at the 12th rib were available for the study. BW was taken and adjusted at birth, 6, 12, 18, 24, 30, and 36 months while RF and EMA scans were taken at 12 & 18 mos. Genetic parameters were estimated with a pedigree-based multi-trait animal model using ASREML software. Results: The average BW of buffaloes adjusted to 6, 12, 18, 24, 30, and 36 months of age were 135kg, 230.8kg, 323.0kg, 382.7, 453.6, and 502.4kg, respectively. The mean EMA and RF at 12 months were 29.7 cm2 and 3.2mm, respectively. Direct genetic heritabilities were 0.26±0.16, 0.29±0.12, 0.35±0.19 and 0.67±0.32 for birth, BW12mos, EMA12mos and RF12mos, respectively. Maternal genetic heritability for birth weight. was 0.12±0.09. Genetic correlations among traits were positive except between birth and RF. Conclusions: Results indicate that ultrasound scanning can be used for selection together with BW at 12mos. and suggest that early performance is an excellent predictor of performance at a later age.
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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 | 0.001 | 0.003 |
| 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".