External preference mapping: A guide for a consumer‐driven approach to apple breeding
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
Abstract This research enabled the creation of a predictive tool to determine consumer preference based on sensory characteristics and to understanding consumer liking for a large and genetically‐diverse apple population. Over two consecutive years, 71 and 83 apples were profiled using descriptive analysis for aroma, taste, and texture attributes. Sensory maps were created, which clustered apples into four groups with common profiles: aromatic‐sweet, acidic, balanced, and mealy. Acceptance data from 219 consumers was collected on a representative subset of 19 apples and related to the sensory properties through external preference mapping. Two consumers groups were identified both preferring juicy, crisp apple but differing in preference for fresh red apple aroma and sweetness (Group 1, 89%) versus more acidic apples with fresh green apple aroma (Group 2, 11%). For both groups, mealy texture was a strong detractor of liking. Preferred sensory characteristics did not differ based on consumer age, gender, or ethnic heritage. Practical applications Understanding consumer preference for fresh apples is key to developing and selecting novel cultivars that will achieve marketplace success. Results are discussed with relevance to a consumer‐driven approach to apple breeding and evaluation of new varieties for commercialization to meet the needs of an evolving apple market.
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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 | 0.001 | 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