Investigation of Blood Protein Polymorphism and Estimation of Genetic Distances in Some Dog Breeds in Turkey
Bibliographic record
Abstract
Phylogenetic relationships in some Turkish dog breeds were investigated to determine protein polymorphisms by electrophoretic analysis. Blood samples were collected from 276 dogs including the Kangal, Akbash and German Shepherd as well as Doberman Pinscher, Setter, Pointer and Labrador breeds. Enzymes and proteins were separated electrophoretically using isoelectrofocusing gel, starch gel and polyacrylamide gel. Polymorphism on albumin (Alb), postalbumin-1 (Poa-1), postalbumin-3 (Poa-3), transferrin (Tf), and esterase (ArE) loci was detected, while no polymorphism was observed on the hemoglobin (Hb) locus. These polymorphisms were used to estimate the average heterozygosity value (h-s), F-statistics and the number of gene flow in each generation (Nm). The genetic distances (dij) were also compared among these dog breeds. Average heterozygosity values were in the range 0.32 (Doberman Pinscher) to 0.41 (Kangal Shepherd), and significant differences in heterozygosity were found among the breeds (P < 0.05). F~ISw, F~ITw and F~STw values were estimated as 0.085, 0.083 and 0.160, respectively, for whole loci in the breeds and these values were significant at P < 0.001. The estimated values of genetic distance in populations other than the Setter breed were between 0.013 and 0.242. Cluster analysis (UPGMA) results showed that the Pointer and Akbash breeds formed a cluster and then the German Shepherd joined this cluster. Finally, the Labrador breed also joined this cluster. However, the Doberman and Kangal Shepherd breeds formed a different cluster. The Setter breed did not join either and formed its own cluster. The formation of 2 distinct clusters in Kangal and Akbash Shepherd dogs reveals that these breeds have different genetic structures in terms of the investigated loci and they were not closely related to each other.
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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 | 0.002 | 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".