Quality Characteristics of Five Disease-ResistantApple Cultivars
Bibliographic record
Abstract
The integration of disease-resistant cultivars (DRCs) into commercial apple production offers a realistic approach for reducing pesticide use in fruit production. Appearance and flavor are key attributes determining whether consumers will accept new cultivars. Five DRCs, ‘NY74828-12’, ‘NY75414-1’, ‘NY65707-19’, ‘liberty’, and ‘McShay’, were tested at harvest over two seasons (1994,1995). In 1994, chemical and physical characteristics of five cultivars were significantly (P ≤ 0.05) different except glucose, total sugar content, and Hunter L and b values. Sensory scores were significantly different in firmness, sweetness and tartness. Cultivar ‘707’ was more preferred in appearance but no significant (P ≤ 0.05) difference was found in flavor and overall acceptance. In 1995, cultivar ‘414’ had lower titratable acidity, Hunter L, a, b values, hue angle and chroma. Cultivar ‘707’ had significantly higher peak force both peeled and unpeeled. Sensory scores were significantly different in firmness. Cultivar ‘414’ had higher preference scores in both flavor and overall acceptance while appearance was equally preferred for all five cultivars. Correlations indicated that apple flavor was highly correlated with percent soluble solids content in 1994 (r = 0.882), and total sugar content in 1995 (r = 0.904). Flavor was also highly correlated with overall acceptance in both 1994 (r = 0.895) and 1995 (r = 0.991).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".