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Record W3181939405 · doi:10.1080/03610470.2021.1937460

Unfilterable Beer Haze Part I: The Investigation of an India Pale Ale Haze

2021· article· en· W3181939405 on OpenAlex
Margaux Huismann, Fraser J. Gormley, Dzeti Dzait, R. Alex Speers, Dawn L. Maskell

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 · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHazeBrewingPolyphenolFood scienceChemistryYeastGlucanBiochemistryFermentationAntioxidantOrganic chemistry

Abstract

fetched live from OpenAlex

The nature of undesirable and unfilterable haze particles observed by craft breweries remains nebulous and presents a challenge when the aim is the production of bright beer. A commercial beer was studied in which the brewery had sporadically encountered unfilterable haze. In this study, it was hypothesized that unfilterable haze particles were formed due to increased concentrations of proteins, polyphenols, and/or beta-glucans. Samples of a high haze and low haze India Pale Ale were degassed and digested with enzymes amyloglucosidase, pepsin, and UltraFlo Max (NovozymesTM). Additionally, the protein, polyphenol, and beta-glucan content of each sample was measured. When comparing protein, polyphenol, and beta-glucan concentrations substantial differences between high haze and low haze protein concentrations were observed. Due to the unfilterable nature of these hazes, combined with experimental findings, it was hypothesized that yeast cell-wall proteins were responsible for this haze. Understanding of the source of these hazes offers brewers the opportunity to mitigate against their formation by adjusting brewing practices.

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 score0.202
Threshold uncertainty score0.110

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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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