MétaCan
Menu
Back to cohort
Record W2758954292 · doi:10.1094/asbcj-2017-4976-01

Malt Modification and its Effects on the Contributions of Barley Genotype to Beer Flavor

2017· article· en· W2758954292 on OpenAlex
Dustin Herb, Tanya Filichkin, Scott Fisk, Laura Helgerson, Patrick M. Hayes, A. P. Benson, Veronica Vega, Daniel Carey, Randy Thiel, L. Cistué, R. Jennings, Robert Monsour, Sean Tynan, Kristi Vinkemeier, Yueshu Li, Andrew Nguygen, Aaron Onio, Brigid Meints, Matthew Moscou, I. Romagosa, William Thomas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Society of Brewing Chemists · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsCanada Malting (Canada)
Fundersnot available
KeywordsBrewingFlavorFood scienceSweetnessSensory analysisBiotechnologyBiologyFermentation

Abstract

fetched live from OpenAlex

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.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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