Use of Digital Images for Evaluation of Factors Responsible for Visual Preference of Apples by Consumers
Bibliographic record
Abstract
This research used digital images to explore some of the factors responsible for consumer preference of visual characteristics of apples ( Malus × domestica Borkh.). The images systematically varied in color and shape (Expt. A: 9 images) and type, shape, and background color (Expt. B: 10 images), while keeping apple size constant. Visual assessments of the apple images were collected from 144 consumers (Expt. A) and 165 consumers (Expt. B) in British Columbia (BC), Nova Scotia (NS), and New Zealand (NZ) using balanced incomplete block designs. Canadian consumers (BC and NS) preferred red apples over green or yellow. NZ consumers liked equally red and green apples, and preferred both to yellow apples. At all locations, consumers in Expt. A significantly preferred round and conical shaped apples to oblong apples. When the combined effects of type, shape, and background color were evaluated, NZ consumers rated the striped, round apples the highest, and least preferred both round and oblong, blush-type apples with yellow backgrounds. NS consumers tended to prefer blush apples regardless of type and background color, and BC consumers were more accepting of a range of apple types, shapes, and background colors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".