Use of descriptive analysis and preference mapping for early‐stage assessment of new and established apples
Bibliographic record
Abstract
BACKGROUND: This research compared four new apple selections with 16 established apples using descriptive analysis (DA), instrumental analyses and preference mapping, in order to identify suitable selections for commercialization and further research. RESULTS: DA revealed that the new apple selections (PARC1, PARC2, PARC3, PARC4) were very similar in texture/mouthfeel (T) but differed in their flavor (F) and appearance (A) characteristics. Preference mapping revealed that consumers' T preferences were driven primarily by crispness, juiciness and lack of skin toughness, while F preferences were driven by sweetness, lack of tartness and presence of fruity flavor. Consumers' A preferences were driven by a high percentage of red color and degree of striping. The majority of consumers had similar T (82-85%) and F (88-92%) preferences for the early- and mid/late-harvest apples. In contrast, consumers' A preferences were differentiated into three subgroups (60%, 24%, 16%) for the early-harvest apples, but not for the mid/late-harvest apples. The new apple selections were among those most liked for T, F and A. CONCLUSION: This early-stage consumer research confirmed that the new apples were comparable, if not superior, to the established apples. As such, it provided the necessary feedback to industry to proceed with commercialization and optimization of cultural and storage practices.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".