Evaluation of <i>Ribes nigrum</i> L. Prospective Hybrids in Estonia
Bibliographic record
Abstract
Abstract The evaluation of promising hybrids of blackcurrant ( Ribes nigrum L.) was carried out in 2017–2019 at Polli Horticultural Research Centre of the Estonian University of Life Sciences, in South Estonia. The objective of the Estonian blackcurrant breeding programme is to produce cultivars that are winter hardy, resistant to gooseberry mildew ( Sphaerotheca mors-uvae (Schw.) Berk.) and gall mite ( Cecidophyopsis ribis Westw.), well suited to machine harvesting, and with good yield and quality of fruits. The evaluation plot was established in the autumn of 2015. Twenty blackcurrant promising hybrids from the Estonian blackcurrant breeding programme and cultivars ‘Pamyat Vavilova’ and ‘Titania’, as the standard, were evaluated for beginning of flowering and fruit ripening (expressed in growing degree days, GDD), winter hardiness, resistance to diseases and pests (expressed in scores 1–9), number of fruits per cluster, drop of flowers and young fruits (%), yield (kg per bush), weight of fruit and content of the soluble solids (°Brix). Fruits were analysed for titratable acids, anthocyanins and polyphenols. The evaluation revealed the best blackcurrant genotypes to be: 1-11-3 (‘Asker’ × ‘Intercontinental’), 6-11-3, 6-11-5 (‘Karri’ × ‘Pamyat Vavilova’) and 7-11-5 (‘Karri’ × SRI 9154-3). All these genotypes are winter hardy and visually resistant to gall mite and gooseberry mildew, and produced good yields and large fruits.
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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".