Registration of the S2MET Barley Mapping Population for Multi‐Environment Genomewide Selection
Bibliographic record
Abstract
Market changes in the malting and brewing industries have increased the demand for locally produced barley (Hordeum vulgare L.) in many regions across North America. Breeding for productive barley cultivars in diverse growing environments is complicated by genotype × environment interactions (GEIs), which can make selection for broad adaptation difficult but may be exploited to select optimal cultivars for each environment. Genomewide selection has recently become a useful tool to make efficient selections on individuals using genomewide marker data. To support the use of genomewide selection to breed locally adapted barley cultivars, the University of Minnesota barley breeding program is publicly releasing a panel of two‐row barley lines, and accompanying data, called the S2MET (Spring Two‐Row Multi‐Environment Trial) (Reg. No. MP‐2, NSL 526938 MAP). The S2MET includes 233 breeding lines grouped into a 183‐line training population and a 50‐line validation population. The entire panel was genotyped using genotyping‐by‐sequencing and phenotyped for 14 important traits in 44 location‐year environments between 2015 and 2017. All data are freely available at the Triticeae Toolbox ( https://triticeaetoolbox.org/barley/ ), and we describe several on‐tap projects and breeding advances that are exploiting this resource. We believe this panel and dataset will be useful for answering important breeding questions related to genomewide selection and GEIs and developing locally superior barley cultivars.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".