EVALUATION OF SWEET CHERRY CULTIVARS RECENTLY INTRODUCED INTO BULGARIA COMPARED WITH TWO BULGARIAN CULTIVARS
Bibliographic record
Abstract
From 1 998 t o 2 00 3, e ight s weet c herry v arieties w ere e valuated i n t erms o f vigor, productivity and fruit weight. The varieties tested were: ‘Celeste’, ‘Vanspur’, ‘Lapins’, ‘Kordia’, ‘Regina’, 13S2717, ‘Bigarreau Burlat’ and ‘Van’. ‘Celeste’, ‘Vanspur’, ‘Lapins’, ‘Kordia’ and ‘Regina’ are recently introduced foreign cultivars. 13S2717 is a hybrid developed at the Summerland Experimental Station in Canada. ‘Bigarreau Burlat’ and ‘Van’ are cultivars commonly grown in Bulgaria. Six o r s even t re es o f e ach v arie ty, g rafted o n P 1 ( Prunus mahaleb seedling) rootstock, were planted 4.5 x 6.0 meters apart. Each tree was trained with a central leader and a freegrowing crown. Gravity irrigation was employed. Growth in ‘Kordia’ and ‘Regina’ was vigorous, and growth in the other varieties was moderate or moderatetovigorous. The most precocious variety was ‘Vanspur’. The m ost p roduct ive v ariety w as ‘Van’ , f ollowed b y ‘Vanspur’ , 1 3S2717, ‘Kordia’, ‘Lapins’, ‘Celeste’, ‘Bigarreau Burlat’ and ‘Regina’. ‘Regina’ blossomed very late. In 2003, ‘Regina’ blossomed very rapidly under adverse weather conditions. The air temperature was over 25°C, which reduced blossom and fruit set. The varieties with the largest fruits were ‘Regina’ and ‘Celeste’. The variety most susceptible to fruit skin cracking was 13S2717.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".