Fruit quality of sweet cherry cultivars in superintensive orchards
Bibliographic record
Abstract
The fruit quality of 15 sweet cherry cultivars (’Canada Giant’, ’Celeste’, ’Chelan’, ’Ferrovia’, ’Germersdorfi Rigle’, ’Katalin’, ’Karina’, ’Kordia’, ’Linda’, ’Regina’, ’Sam’, ’Sandra Rose’, ’Sunburst’, ’Sylvia’ and ’Techlovan’) was studied under super-intensive growing conditions at Nagykutas. We measured the fruit diameter, fruit width, fruit height, stem length and stem weight, fruit and pit weight and the total dry matter content. There were large differences among the cultivars. These differences are due to the genetic characteristics of fruits because all other conditions were the same. For 11 cultivars, we collected fruit samples several times /2-4/. We examined on this cultivars all the above listed fruit quality parameters. When examining these samples, we have gained information how earlier or later than optimal harvest time influences fruit quality.
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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 | 0.000 | 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) | 0.001 | 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".