Application of microsatellites in population genetic studies of reindeer (Rangifer tarandus) (review)
Bibliographic record
Abstract
Over a few past decades, theoretical, analytical, and methodological advances in genetics have revolutionized population genetic research, providing a better understanding of evolutionary processes and the history of populations and species. Methodologically, this progress is largely due to the invention of polymerase chain reaction technology and the introduction of microsatellite DNA markers. This review discusses trends in the use of microsatellite markers as effective tools for solving a wide range of issues in population genetics, conservation and evolutionary biology of the only species of the genus Rangifer – reindeer. Based on the analysis of both experimental and review publications (78 sources) of the scientific teams of the Russian Federation, Canada, the United States of America, Ireland, Japan, China, Norway the first works on the successful amplification of reindeer microsatellites have been summarized. There has been demonstrated the significance of the data of markers for studying intra- and inter-population diversity, differentiation, genetic relationships, the impact of anthropogenic factors on genetic diversity and genetic isolation of populations, as well as for reconstructing the evolutionary history of the various reindeer forms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".