Genetic diversity analysis of 119 Canadian maize inbred lines based on pedigree and simple sequence repeat markers
Bibliographic record
Abstract
Reid, L. M., Xiang, K., Zhu, X., Baum, B. R. and Molnar, S. J. 2011. Genetic diversity analysis of 119 Canadian maize inbred lines based on pedigree and simple sequence repeat markers. Can. J. Plant Sci. 91: 651-661. Since the early 1920s Agriculture and Agri-Food Canada (AAFC) has been developing maize varieties and inbred lines adapted to the early maize growing regions. These inbreds represent a large group of genetically diverse genotypes; however, many of the inbreds are of unknown heterotic backgrounds. The purpose of this study was to classify 119 elite maize inbred lines released from AAFC into heterotic groups using both pedigree data and simple sequence repeat (SSR) markers and also to explore the consistency among different classification analyses. Pedigree analysis placed the inbred lines into eight groups, six of which corresponded to known major heterotic groups representing Iowa Stiff Stalk Synthetic (BSSS), European flint, Lancaster, Minnesota 13, Early Butler, and Iodent; the two remaining groups consisted of germplasm derived mostly from Pioneer 3990 or Pioneer 3994 sources. Simple sequence repeat analysis of 105 loci resulted in a clustering of the inbreds into 10 groups. In comparison with the grouping based on pedigree, the SSR clustering groups had some discrepancies and groups of genetically similar germplasm, based on pedigree, could not always be confirmed with molecular markers. The results of this study will allow researchers and maize breeders to make more informed decisions on the use of these inbreds in breeding programs.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".