Malt Modification and its Effects on the Contributions of Barley Genotype to Beer Flavor
Bibliographic record
Abstract
Based on prior research that showed significant genetic differences between barley genotypes for beer sensory descriptors, the effects of degree of malt modification on these descriptors were assessed in two experiments. The first experiment involved sensory assessment of nano-beers made from micromalts of Golden Promise, Full Pint, 34 doubled haploid progeny, and the check CDC Copeland. Average degree of modification was assessed by sampling grain from each of the 37 genotypes stored for three postharvest intervals prior to malting and brewing. The second experiment involved sensory assessment of pilot beers made from intentionally under-, properly, and overmodified pilot malts of two barley varieties: Full Pint and CDC Copeland. In both experiments, genotypes were the principal sources of significant variation in sensory descriptors. Degree of modification and genotype × modification interactions were also significant for some descriptors. Based on the results of this study, the genetic characterization of and selection for barley contributions to beer flavor are warranted, even with undermodified malts. The contribution of barley variety to beer flavor will likely be modest compared with the flavors developed during the malting process and the flavors contributed by hops and yeast. However, in certain beer styles, the contributions of barley genotype may be worth the attention of maltsters, brewers, and consumers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".