Simple sequence repeat-based identification of Canadian malting barley varieties
Bibliographic record
Abstract
Perry, D. J., Fernando, U. and Lee, S-J. 2014. Simple sequence repeat-based identification of Canadian malting barley varieties. Can. J. Plant Sci. 94: 485–496. Practical and reliable means to identify barley varieties are required to provide assurances in segregated grain handling and for quality control in the malting and brewing industry. A set of 10 simple sequence repeat (SSR) markers was selected to differentiate among malting barley varieties grown in Canada. Modification of some PCR primers permitted assembly into two five-marker multiplexes that may be examined simultaneously using an electrophoresis-based DNA analyzer. These markers were surveyed in multiple individual kernels of each of 48 barley varieties grown in Canada, including 31 malting varieties and 17 popular feed varieties. Variation within varieties was common and three general categories of intra-variety polymorphism were recognized: (1) primary biotypes, which were characterized by a fairly even distribution of two alleles at one or more marker loci and complete mixture of allele combinations among the polymorphic loci; (2) uncommon, distinctly different variants; and (3) putative recent SSR mutations. Differentiation among varieties was complete with the exception of one pair of related six-row feed varieties (AC Rosser and AC Ranger) that was indistinguishable and one group of three very closely related two-row malting varieties (CDC Kendall, CDC PolarStar and Norman) that, on an individual-kernel basis, were only partially distinguishable using these markers. Simple, rapid individual-kernel DNA preparation methods were also developed for use in conjunction with the multiplexed markers to provide a convenient, effective and relatively inexpensive tool that may be used for barley variety identification, purity analysis or quantification of variety mixtures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".