Evolution of a quality testing program for improving malting barley in Canada
Bibliographic record
Abstract
Edney, M. J., MacLeod, A. L. and LaBerge, D. E. 2014. Evolution of a quality testing program for improving malting barley in Canada. Can. J. Plant Sci. 94: 535-544. The quality of Canadian malting barley has continually improved since malt barley was exported in the late 1800s. Improvements were linked to a dynamic evaluation system that evolved with a better understanding of malting biochemistry and as suitable methods were developed. Methods became more accurate and more specific in their ability to define quality. They progressed from sensory evaluation, to surmising malt quality from barley protein levels, to the first micro-maltings followed by automated laboratory-scale maltings. Malt quality analysis started simply with malt extract and diastatic power followed by wort protein. As the necessity for cell wall breakdown became better understood, analyses like wort viscosity, fine/coarse grind extract differences and wort β-glucan were adopted. A continuum of cultivars were released in Canada, based on this evaluation system, starting with the six-rowed releases OAC 21, then Montcalm and Bonanza, followed by the two-rowed releases Betzes, Klages, Harrington and AC Metcalfe. Release of future cultivars will depend on an evolving evaluation system that could include; barley homogeneity, specific starch-degrading enzymes, individual amino acids and specific traits such as low lipoxygenase and low phytic acid barley. The result will be development and release of cultivars with better defined quality that can fill specialized niches in the malting and brewing industries of the future.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".