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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".