Evaluation of foreign remontant raspberry cultivars for production and breeding
Bibliographic record
Abstract
The over 100 remontant raspberry cultivars worldwide continue to variegate, with breeding mostly successful in the USA, Canada, Great Britain, Poland, Switzerland, the Netherlands and Russia. In most native climates of Russia, foreign varieties unluckily do not realise their biological potential to not attain the originator-declared character. The research aimed at a comprehensive study of introduced remontant raspberry cultivars to clarify the prospects of their production and breeding. The study was being conducted over 2018-2020 on the genetic raspberry collection plot of the All-Russian Horticultural Institute for Breeding, Agrotechnology and Nursery’s Kokino base station (Bryansk Region). Research focused on ten foreign remontant raspberry cultivars. The late-maturing Atlant variety of the state-permitted crop catalogue served a control. Research followed the generally accepted protocols. Statistical experimental data analyses were accomplished with Microsoft Excel. Phenotypic evaluation of the introduced remontant raspberry cultivars by plant morphology revealed their Middle Russia-specific traits of growth and development. The greatest yield surface (238-316 cm) was observed in Joan J, Imara, Himbo Top and Carolina foreign cultivars. The remontant varieties studied were found to distinguish by late maturity and low yield (2.9-6.1 t/ha), thusly being off-focus to industrial horticulture. Imara, Erika, Sugana, Joan J, Carolina and Himbo Top leading with a 5.0-6.1 t/ha yield can be recommended in home gardening. Selected foreign cultivars deserve attention as a genetic resource in breeding for larger fruit size (Poranna Rosa, Driscoll Maravilla, Sugana), higher soluble solid content (Kweli, Karolina, Kwanza), fruit strength (Kwanza, Driscoll Maravilla), optimal fruit detachment force (Himbo Top, Joan J, Imara, Kwanza) and compact bush habitus (Poranna Rosa).
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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 | 0.001 | 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".