ASSESSMENT OF PHYSICAL AND CHEMICAL CHARACTERISTICS OF SWEET CHERRY FRUITS: A COMPARATIVE ANALYSIS OF SIX DISTINCT CULTIVARS
Bibliographic record
Abstract
Sweet cherry (Prunus avium L.) is a member of the Rosaceae family and is valued for its distinctive flavoured fruits and significant nutritional and medicinal properties. This study aimed to evaluate the physical and chemical characteristics of cherry fruits of six different cultivars: ‘Regina’, ‘Kordia’, ‘Bigarreau Burlat’, ‘Merchant’, ‘Canada Giant’ and ‘Sylvia’. The quality parameters determined were fruit length, width and thickness, the shape index; fruit and stone weight to calculate pulp-stone ratio; fruit firmness; total soluble solid content, fruit water content and pH. The results highlighted the variability between cultivars, providing information on their morphological and physico-chemical characteristics. Shape index and pulp-stone ratio reflect differences in fruit structure and composition, critical aspects for consumer preferences and industrial applications. Total soluble solids content and water content define the level of fruit sweetness and juiciness, while firmness and pH contribute to sensory characteristics and post-harvest quality. Therefore, understanding the cultivar-specific traits and their implications for horticultural practices, marketing and consumer acceptance remain important. The results are valuable both for growers and food industry in optimizing the production and use of cherries.
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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 | 0.000 | 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".