Identification of quantitative trait loci for milk yield in Murrah buffaloes
Bibliographic record
Abstract
A reference family consisting of 12 half sib sire families were created for the identification of QTLs for milk yield in buffaloes. Daughters were recorded for monthly test day milk yield. The number of daughters per sire varied from 50 to 335 daughters per sire. Seventy nine polymorphic microsatellite markers located on 8 chromosomes were genotyped for 2281 daughters of the 12 sires. Whole chromosome scanning was done using single marker analysis and interval mapping using three different algorithms. The analysis was carried out sire family wise. QTLs (63) were identified in single marker analysis and 32 QTLs were identified using interval mapping. The significance of LOD score was tested using permutation tests. The metaQTL analysis was carried out to find out the consensus chromosomal regions associated with milk yield in buffaloes. Five models were utilised and the best was selected on the basis of Akaike Information content. Total 23 chromosomal regions were identified for milk yield in buffaloes. 2 metaQTL chromosomal regions were identified on buffalo chromosome BBU2q; 3 metaQTLs each on buffalo chromosomes BBU8, BBU10 and BBU15 and 4 metaQTL regions each on BBU1q, BBU6, BBU9.
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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 | 0.000 | 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) | 0.001 | 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".