Barley variety interacts positively with floor malting to produce different malts and beers
Bibliographic record
Abstract
Why was the work done: Floor malting maintains a small but notable market share due to its reputed contributions to beer flavour. These malts are viewed as premium products and are produced in both historic and contemporary floor maltings. Despite this, little work has been performed on floor malting to evaluate its effect on malt and subsequent beer quality and flavour. Accordingly, this work investigated whether floor malting produces distinct malts and beers relative to pneumatic maltings. How was the work done: A mini-floor malting protocol was developed to malt small quantities of grain in a repeatable system that produces malt comparable to the production scale. Two winter barley varieties (Lontra and Thunder) were used to understand whether there was a malting type by variety interaction effect on beer flavour. What are the main findings: Both floor and pneumatic malts produced similar malts and beers based on quality metrics and the differences found between malts were more attributable to variety and the respective rate of proteolysis. Sensory results showed that there was a significant malting type by variety interaction driving hedonic and descriptive sensory results. Why is the work important: These results suggest that while the different malting types produce analytically similar malt, selection of barley variety can be used to optimise the floor malting process to produce distinct beer flavour profiles.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".