PSI-9 Molecular characterization of Berganês sheep, a locally adapted ecotype from Brazilian semi-arid region
Bibliographic record
Abstract
Abstract The Ovis aries species is phenotypically diverse and it is bred around the world for meat, milk and wool production. In the 1980s, small farmers in the semi-arid region of Pernambuco, Brazil, initiated the introgression of genes from Santa Inês and Bergamácia breeds in their local sheep of undefined breed, and the selection of individuals in an unstructured form, giving rise to an ecotype with unique characteristics called Berganês. The aim of this study was to perform genetic structure analysis of the Berganês ecotype population on farms in the state of Pernambuco using the Illumina® BeadChip OvineSNP50 high density chip as a way of increasing knowledge about the ecotype. Animals from seven farms were genotyped, totaling 96 animals (17 males and 79 females). Of the 54,241 Single Nucleotide Polymorphism (SNPs) found, we elected the ones with GenCall Score > 0.5, Hardy-Weinberg equilibrium (significance at 0.01) and lower allele frequency (MAF) > 0.2. In addition, only SNPs located on the autosomal chromosomes were maintained, according to version 4.0 of the sheep genome, with 39,250 SNPs being selected. The observed and expected mean heterozygosity values were, respectively, 0.37159 and 0.37943. The F statistics found were: FIS = 0.02622, FST = 0 and FIT = 0.02394. Most of the variability found (97.61%), which was estimated by AMOVA, is uniformly distributed within the herds and the Principal Component Analysis (PCA) did not allow the visual identification of a substructure considering herds, sex or phenotypic characteristics (coat color, ear size and insertion, and head morphology). Thus, the genetic variability presented in the animals of the Berganês ecotype is distributed homogeneously among the herds analyzed. Therefore, the genetic characterization presented here represents a key point in the creation of conservation plans and breeding programs, improving the efficiency of selection processes and the selection of breeders.
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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 | 0.001 | 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".