Qualitative Properties of the Fruits of Blackcurrant Ribes Nigrum L. Genotypes in Conventional and Organic Cultivation
Bibliographic record
Abstract
Abstract The aim of the investigation was to evaluate the effect of genotype and growing conditions on blackcurrant fruit weight and biochemical composition. The trial was carried out during the years 2011 and 2012 in South Estonia with two cultivation methods (conventional and organic) and eight genotypes of different genetic background, including two Scottish, ‘Ben Alder’ and ‘Ben Lomond’; two Swedish ‘Intercontinental’ and ‘Titania’; a Belarusian ‘Pamyati Vavilova’, and three recently selected genotypes from the Estonian blackcurrant breeding programme, ‘Karri’, ‘Asker’, and ‘Mairi’. From each genotype and in both cultivation sites 500 g of fruit at full maturity was collected in three replications. Fruit weight, soluble solids (SS), sugars, organic acids, sugar/acid ratio, and ascorbic acid (AsA) concentrations were determined. Fruits from the organic cultivation site were smaller and contained more SS and sugars, they also had a higher sugar/acid ratio than conventionally grown berries. Organic acids and AsA concentrations were higher in berries from conventional cultivation systems compared to the organic ones. ’Karri’ had the highest SS and sugar concentrations and sugar/acid ratio and the lowest concentration of acids on average over the years and cultivation methods. The highest AsA concentration was determined in ‘Asker’ and ‘Ben Lomond’.
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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".