Investigation of growth vigour, yielding and berry quality of the promising raspberry cultivars in Lithuania.
Bibliographic record
Abstract
12 raspberry cultivars developed in Russia, Ukraine, Estonia, England, Canada and USA were investigated at the Lithuanian Institute of Horticulture in 2003–2006. The most winterhardy were standard cultivars ‘Novokitajevskaja’ (stem cold injury – 0.5 scores) and ‘Beglianka’ (stem cold injury – 0.4–0.9 scores). Stems of raspberries ‘Meeker’ (2.6–4.5 scores) and ‘Glen Moy’ (2.2–3.7 scores) were the most cold injured. The most productive raspberry cultivars were ‘Siveli’, ‘Novokitajevskaja’, ‘Zorinka’, ‘Beglianka’, ‘Sputnica’, ‘Zviozdocka’ and ‘Husar’ (5.08–4.11 t ha-1), the least productive ones – ‘Meeker’ and ‘Glen Moy’ (1.81–2.87 t ha-1). The biggest berry weight was of cultivars ‘Glen Moy’, ‘Aborigen’, ‘Miraz’ and ‘Meeker’ (2.04–2.68 g). Berries of cultivar ‘Otava’ distinguish themselves with the significantly biggest amount of dry soluble solids (13.7), bigger amount of sugars (7.02%), ascorbic acid (24.4 mg 100g-1) and anthocyanins (22.4 mg 100g-1). In the berries of cultivar ‘Glen Moy’ it was found the bigger amount of anthocyanins (36.96 mg 100g-1) and ascorbic acid (23.6 mg 100g-1). The berries of cultivar ‘Husar’ distinguish themselves with big amount of dry soluble solids (11.8), ascorbic acid (20.4 mg 100g-1) and anthocyanins (32.03 mg 100g-1). The berries of cultivars ‘Miraz’ and ‘Meeker’ distinguish themselves with big amount of ascorbic acid – 24.80 mg 100 g-1 and 24.4 mg 100 g-1, respectively.
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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 | 0.001 |
| 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 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".