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Record W6959667696 · doi:10.1139/cjps2013-118

Evolution of a quality testing program for improving malting barley in Canada

2014· article· en· W6959667696 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsBrewingCultivarQuality (philosophy)Phytic acidSensory analysisGrind

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.247
GPT teacher head0.236
Teacher spread0.011 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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