Assessment of genetic diversity and genetic relationships of farm and laboratory quail populations in Japan using microsatellite DNA markers
Bibliographic record
Abstract
Abstract Background The Japanese quail ( Coturnix japonica ) is an important poultry species owing to their high economic efficiency and biological advantages. The genetic diversity of farm quail populations has rarely been studied. Objectives This study aimed to assess the genetic diversity of farm quail populations and their genetic relationships, which could provide important information for designing breeding programmes to maintain egg and/or meat production efficiency. Methods Molecular phylogenetic and STRUCTURE analyses were conducted for seven farm populations and six laboratory lines using 50 microsatellite markers previously developed by us. Results The genetic diversity within each farm population was relatively high despite long‐term breeding within closed colonies. However, the genetic variation between populations was absent. Twenty highly polymorphic markers, selected based on Ne , He and F ST values, enabled the construction of reliable phylogenetic trees and STRUCTURE plots. Conclusions In the farm populations analysed in the present study, gene flow between genetically distant populations is needed to restore genetic diversity between farm populations, which could exploit heterosis and decrease the risk of inbreeding depression. Our findings demonstrate that these markers are useful for examining the genetic structure of farm quail populations.
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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 | 0.000 |
| 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".