Evaluation of the genetic diversity and population structure of five indigenous and one introduced Chinese goose breeds using microsatellite markers
Bibliographic record
Abstract
Li, J., Yuan, Q., Shen, J., Tao, Z., Li, G., Tian, Y., Wang, D., Chen, L. and Lu, L. 2012. Evaluation of the genetic diversity and population structure of five indigenous and one introduced Chinese goose breeds using microsatellite markers. Can. J. Anim. Sci. 92: 417–423. The aim of this study was to determine the genetic diversity and evolutionary relationships among five indigenous Chinese goose breeds and one introduced goose breed using 29 microsatellite markers. A total of 334 distinct alleles were observed across the six breeds, and 45 of the 334 alleles (13.5%) were unique to only one breed. The indigenous geese showed higher diversity in terms of the observed number of alleles per locus (4.48–5.90) and observed heterozygosity (0.46–0.53) compared with the introduced breed (3.97 and 0.29, respectively). The pairwise genetic differentiation (FST) between the six goose breeds ranged from 0.04 between Panshi Grey goose (PS) and Yongkang Grey goose to 0.47 between PS and Landes goose; similarly, Nei's genetic distance varied between 0.25 and 0.75. However, the FSTbetween the indigenous Chinese goose breeds was very small. In addition, genetic distance estimate, phylogenic, and cluster analyses of the genetic relationships and population structure revealed that some indigenous goose breeds had hybridized more frequently, resulting in a loss of genetic distinctiveness.
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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 | 0.001 | 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) | 0.000 | 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".