Depicting Soybean Diversity via Complementary Application of Three Marker Types
Bibliographic record
Abstract
Driven by the growing demands for plant-based protein in Europe and attempts of soybean breeding programs to improve the productivity of created varieties, this study aimed to enhance genetic resource utilization efficiency by providing information relevant to well-focused breeding targets. A set of 90 accessions was subjected to a comprehensive assessment of genetic diversity in a soybean working collection using three marker types: morphological descriptors, agronomic traits, and SSRs. Genotype grouping patterns varied among the markers, displaying the best congruence with pedigree data and maturity for SSRs and agronomic traits, respectively. The clear origin-related grouping pattern was not observed for any of the marker types. For the diversity assessed by morphological descriptors, Homogeneity Analysis by Means of Alternating Least Squares (HOMALS) yielded the most efficient classification by identifying the traits with the highest discriminative power and separating the genotypes into homogeneous groups. According to genetic distances (GDs), the highest diversity was found for morphological descriptors (GD = 517), followed by SSRs (GD = 0.317) and agronomic traits (GD = 0.244). The analysis of molecular variance (AMOVA) revealed a weak differentiation between geographic groups (ΦST = 0.061), emphasizing the highest differentiation for Canadian genotypes (ΦST = 0.148 **). A low correlation was found between molecular and morphological, i.e., agronomic trait-based matrices (0.061 *, i.e., –0.027, respectively). The overall assessed diversity highlighted the importance of introducing new sources of variation to promote long-term improvement in soybean breeding.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".