Investigation of growth vigour, yielding and berry quality of the promising raspberry cultivars in Lithuania.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| 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 it