Bibliographic record
Abstract
The purpose of this research was to understand and evaluate the effect of high temperature treatment of wort at varying time periods (30, 45, 60, 90, and 120 min) on wort fermentability. The effect of trub was also analyzed. Small-scale fermentations were conducted using a malt (malted from AC Metcalfe) free of premature yeast flocculation tendencies and a standard SMA yeast strain. The turbidity and apparent extract were measured at specific time intervals. The decline in extract was modeled with the ASBC Yeast-14 logistic equation predicting a sigmoidal curve. Turbidity measurements were modeled with a “tilted Gaussian” fit. Heat-treating the wort (at 100 or 121.1 °C) resulted in a significant decline in ADF (p < 0.05). As denoted by the midpoint of the fermentation, all fermentation runs, with and without trub, at lower wort boil durations fermented faster than longer or higher wort-boiling treatments. The decline in wort fermentability was highest upon treatment at wort temperature and pressure levels of 121.1 °C and 3.0 atm. The amount of trub and the wort color formed after each heat treatment showed a gradual increase with heating duration. Free amino nitrogen levels of wort declined significantly with respect to both increase in wort boiling temperature and time intervals (p < 0.001).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".