The Use of Temporal Check-All-That-Apply and Category Scaling by Experienced Panellists to Evaluate Sweet and Dry Ciders
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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