Investigating the use of partial napping with ultra-flash profiling to identify flavour differences in replicated, experimental wines
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.
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it