Application of microsatellite DNA primers for the analysis of the genetic variability of Lithuanian native goose breeds
Bibliographic record
Abstract
2 Institute of Ecology, Vilnius University, Akademijos g. 2, LT-08412, Vilnius, Lithuania The aim of our study was to assess the use of waterfowl species specific primers in order to detect polymorphism in Lithuanian native geese breeds (Vistinės, Skarulės and Vistinės-Skarulės hybrids – Native Mixed). Also, the White-fronted geese species was investigated for comparison. The microsatellite DNA analysis was carried out using 11 microsatellite primers from which only 4 gave a positive PCR product: Sfimu1 (some wild waterfowl species specific marker), TTUCG-1, TTUCG-2, TTUCG-4 (Canada geese specific marker). According to our data, it is possible to use wild waterfowl specific microsatellite DNA markers for a comparative microsatellite DNA analysis of the White-fronted geese species and 2 Lithuanian breeds (Vistinės and Skarulės), and their hybrids (Native Mixed) were obtained by interbreeding these breeds. We found that the TTUCG-1 primer, due to monomorphic PCR products, was not suitable for the population analysis of Skarulės and the Vistinės geese breeds. Due to the absence of the amplified product, the TTUCG-4 primer is not suitable for hybrid geese microsatellite DNA analysis.
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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 | 0.002 | 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) | 0.001 | 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".