Biplot Simulation of Length and Circumference of Different Body Regions of Swamp Buffalo (Bubalus bubalis)
Bibliographic record
Abstract
Background: Biplot simulation using factor analysis with Promax kappa rotation of 90 is used to determine the coordinates of the length and body circumference dimensions of male and female mud buffalo in two-dimensional space. So that it can be known which body dimensions have fast, medium, and slow growth rates, and also to find out at what age male and female buffalo the growth rate is fast or slow. Methods: This study used 3 male and female buffaloes aged 10, 12, 14, 16, 18, and 20 months, so that the number of buffalo used was 36. The data obtained were analyzed using Factor Analysis with Promax Kappa 90 rotation. The object coordinates were the coordinates of male and female swamp buffalo aged 10, 12, 14, 16, 18, and 20 months, determined based on the Analysis Factor Scores Regression. The location of the coordinates of the length and circumference dimensions and the location of the object coordinates of male and female buffaloes aged 10, 12, 14, 16, 18, and 20 months are drawn using a biplot graph. Conclusion: The biplot simulation results show that the body dimensions in quadrant II have the fastest growth rate, namely the length of the horns and the length of the neck, which are body dimensions that are slow to reach adult size. The dimensions of the body in quadrant I are body length, head length, upper and lower neck circumference. While the slow growth rate in quadrant IV indicates that the dimensions of the body are already growing or the growth is not optimal; namely, the length of the ears and the length of the tail have reached adult size. At the same time, the abdominal circumference and chest circumference are due to their growth not being optimal. Based on age and sex, the dimensions of male buffalo's length and body circumference are always greater than that of female buffalo from the age of 10-20 months. The positional distance between male and female buffalo age shows that the older the age, the shorter the distance; this result indicates that the growth rate slows down with increasing 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".