Study of Population Structure and Genetic Prediction of Buffalo from Different Provinces of Iran using Machine Learning Method
Bibliographic record
Abstract
Considering breeding livestock programs to milk production and type traits based on existence two different ecotypes of Iranian’s buffalo, a study carried out to investigate the population structure of Iranian buffalo and validate its classification accuracy according to different ecotypes from Iran (Azerbaijan and North) using data SNP chip 90K by means Support vector Machine (SVM), Random Forest (RF) and Discriminant Analysis Principal Component (DAPC) methods. A total of 258 buffalo were sampled and genotyped. The results of admixture, multidimensional scaling (MDS), and DAPC showed a close relationship between the animals of different provinces. Two ecotypes indicated higher accuracy of 96% that the Area Under Curve (AUC) confirmed the obtained result of the SVM approach while the DAPC and RF approach demonstrated lower accuracy of 88% and 80 %, respectively. SVM method proved high accuracy compared with DAPC and RF methods and assigned animals to their herds with more accuracy. According to these results, buffaloes distributed in two different ecotypes are one breed, and therefore the same breeding program should be used in the future. The water buffalo ecotype of the northern provinces of Iran and Azerbaijan seem to belong to the same population
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".