Genetic Diversity Analysis of Five Egyptian Buffalo Populations Using Microsatellite Markers
Bibliographic record
Abstract
For assessing the genetic diversity and genetic characterization of five Egyptian buffalo populations a total of 12 microsatellite markers were used. The total number of buffaloes sampled was 80, collected at random from five farms in five different governorates; Cairo, Kafr El-Sheikh, Shebeen El-Kom, Menoufia, and Beni Suef. The genetic parameters (allelic diversity, allelic frequencies, observed heterozygosity, unbiased expected heterozygosity, and polymorphic information content) were calculated using three different programs. All used microsatellites were polymorphic and ranged from four alleles (Loci; CSSM029, CSSM036, CSSM038, CSSM043, CSSM046, and ILSTS005) to nine alleles (Loci; BM1818 and CSSM047) with a total of 64 alleles in the whole population. Allelic richness for the whole population ranged between 3.297 (in locus CSSM029) and 6.806 (in locus CSSM047) with overall mean 4.574. Within populations, Kafr El-Sheikh population had the highest average of allelic richness (4.384). This indicates the potential of this population to adapt with environmental changes in future compared with other populations. BMC1013, BM1818, CSSM019, and CSSM047 showed the highest allelic richness. PIC estimates were high and ranged between 0.65 (in locus CSSM029) and 0.92 (in locus CSSM022) with an average of 0.82. Values of Ho were lower than values of HNb for all populations, which denoting depression of heterozygotes in these populations and may be attributable to existence of null alleles and inbreeding. This study as well proves the usefulness of heterologous bovine microsatellite markers in evaluation of the genetic variability in Egyptian buffalo populations due to high polymorphism, informativeness of these markers which can be used to develop future breeding strategies and conservation decisions on our indigenous breed.
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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 | 0.001 |
| 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) | 0.001 | 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".