Unfilterable Beer Haze Part I: The Investigation of an India Pale Ale Haze
Bibliographic record
Abstract
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".