Associations Between Microsatellites Markers and Growth Traits in Goat
Bibliographic record
Abstract
The rapid progress in the field of biotechnology gives us a great chance to improve the productivity of our native animals in short time, relatively. The situation in Saudi Arabia not differ in compare with other country in our region which have a big challenge to increase the meat production for food security purposes. The situation in goat is very far from the point which other farm animals like cattle reached today, where a complete QTLs maps for most important traits is available. But by the same way, long time ago we have a complete comparative map between goat and both sheep and cattle. According to that in the previous investigation in both sheep and cattle we collected a total of 19 loci distributed in goat different chromosomes along with a total of 1050 records from 525 individual of a crossbred of Aradi and Damascus Goat were used to perform the linkage analysis. The studied traits were body weight at birth, 4, 8, 12, 16, 20, and 24 weeks of age. The association between microsatellite markers and body weights traits were estimated by stepwise partial regression. Twelve markers (BMS2325, BMS332, BM17132, BMS1316, BM1827, BM1225, BMS2142, BM1558, BMS2809, BMS1348, BMC4216 and BM1329) successfully linked to studied traits. These markers are. In general, our work design and plan successfully worked and could explained some of genetic vitiation by using microsatellites markers. Further work to build a complete QTLs map for meat production traits is necessary to make a complete view about this important species.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".