Investigating the use of partial napping with ultra-flash profiling to identify flavour differences in replicated, experimental wines
Bibliographic record
Abstract
Experimental wine studies with three or more treatments, over multiple years with replicated wines, often require sensory analysis to describe treatment effects on the resultant wines. This scientific approach can result in a large number of samples for sensory analysis, which can be time-consuming, and problematic for the design of descriptive analysis (DA). The aim of this study was to establish whether partial napping (PN) combined with ultra-flash profiling (UFP) could identify a subset of replicate wines that were similar enough in flavour profile that they could be used as representative samples for descriptive analysis (DA). Pinot noir wines from three field treatments (T1, T2, and T3), were produced in triplicate (a, b and c) and analysed by PN and UFP. Multiple factor analysis (MFA) using a citation frequency method showed that two similar replicate wines could be identified for each treatment wine. These results show that UFP allows for small sample sets to be used for subsequent and more resource intensive DA methods, and provides greater insight into the use of rapid sensory analysis in wine research.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".