Association Mapping of Malting Quality Data from Western Canadian Two‐row Barley Cooperative Trials
Bibliographic record
Abstract
ABSTRACT Re‐examining historical datasets is a proposed use for association mapping (AM) and is particularly valuable when the data describes time‐consuming and/or expensive to measure traits. A collection of 91 elite two‐row malting barley (Hordeum vulgare L.) lines entered in the western Canadian Cooperative Two‐Row Barley Trials over a 13‐ yr period were analyzed by AM to identify markers associated with seven malting quality traits. A linear mixed‐model incorporating population structure and familial relatedness identified 27 diversity array technology (DArT) markers associated with malting quality. These markers will assist selection of parents with complementary allele combinations for future crosses and help identify progeny with the desired alleles. Putative candidate expressed sequence tags (ESTs) responsible for marker‐trait associations were identified for 19 of the 27 DArT markers and include genes important for seed storage protein accumulation, gibberellin‐mediated gene expression, seed storage mobilization, and dormancy. Efforts to identify novel variability in these genes may present opportunities to improve malting quality.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".