Utilization of microsatellite markers in genotyping of Saudi Arabian camels for productivity and conservation
Bibliographic record
Abstract
Camels are considered as integral and notable components of the heritage of the Kingdom of Saudi Arabia. Genetic variabilities within and among four camel populations in Saudi Arabia were evaluated using 21 simple sequence repeat (SSR) loci of 122 unrelated individuals, including three indigenous breeds [Humur (HA), Zurg (ZR), Shuguh (SG)] and one exotic breed [Sudanese (SN)]. Nineteen SSR markers generated multilocus fingerprints with a total of 225 alleles, a range of 4–23 alleles per locus, and an average of 9, 7, 7, and 6 alleles per locus in HA, ZR, SG, and SN populations, respectively. The mean multilocus FST value (0.034 ± 0.005) showed non-significant population differentiation. Mean observed heterozygosity values were 0.908 for HA, 0.860 for ZR, 0.919 for SG, and 0.887 for SN, which were higher than the expected heterozygosity. An excess of heterozygotes was observed, suggesting the presence of overdominant selection or the occurrence of outbreeding. Pairwise genetic distances indicated that the three indigenous camel breeds were genetically close to each other and genetically distant to the SN population. This genetic variability assessment by microsatellite analysis is important and useful for the conservation of local camel genetic resources as well as the future development of breeding programs.
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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 | 0.001 |
| 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) | 0.000 | 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 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".