Genomic analysis and predictive modeling in the Northern Uniform Soybean Tests
Bibliographic record
Abstract
Abstract The Northern Uniform Soybean Tests (NUST) are a regional field trial network coordinated by the United States Department of Agriculture to evaluate experimental soybean ( Glycine max L.) strains developed by public institutions. Historical data from the NUST compiled, curated, and reported herein comprise a valuable multi‐environment trial dataset including relevant elite soybean germplasm from maturity groups 00 to IV evaluated over 28 years in 199 locations, totaling 1652 environments. Our aim was to characterize the genetic structure of the NUST experimental strains, perform genome‐wide association studies using historical phenotypic data, and assess the usefulness of these historical data for genomic prediction model training. Molecular marker information was collected on 2544 unique NUST experimental strains using the BARCSoySNP6K assay. High fixation index values between early and later maturity groups were observed in a region on chromosome 10 near the known soybean maturity gene E2 . We failed to find strong genetic divergence between strains from different breeding programs, reflecting the germplasm sharing common among public programs. Genome‐wide association analyses on important agronomic traits identified marker‐trait associations, many of which overlap with quantitative trait loci previously reported in the literature. Genomic prediction models trained using the historical NUST data produced moderate to high predictive abilities in most cases, suggesting these data could make useful contributions to training sets. We have made these data publicly available as a data resource for others to study genotype–phenotype relationships within elite public soybean germplasm and develop predictive models for advancement and implementation of genomics‐assisted breeding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 teacher head, 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".