The Use of Temporal Check-All-That-Apply and Category Scaling by Experienced Panellists to Evaluate Sweet and Dry Ciders
Bibliographic record
Abstract
Cider is a growing market in North America, but more studies need to be completed to fully understand ciders’ sensory properties. The primary objective of this study was to identify the differences in the sensory properties of ciders described as “sweet” or “dry” using both static (category scales) and dynamic (temporal check-all-that-apply, TCATA) sensory methodologies. The secondary objective was to evaluate experienced panellists with a familiar methodology (category scales) and an unfamiliar methodology (TCATA). The sweet ciders were characterized by sweet, floral, cooked apple, and fresh apple attributes, and they had a sour aftertaste. The dry ciders were found to be bitter, sour, earthy, and mouldy, and they had a sour and bitter aftertaste. The experienced panellists produced reproducible results using both methodologies; however, they did not find small differences between the cider samples. Future research should investigate a wider range of cider and investigate ciders’ aftertaste. More studies need to be completed on experienced panellists and on when researchers and the food industry should use them.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".