Вихідний матеріал для селекції тютюну сортотипу вірджинія у придністровській зоні України
Bibliographic record
Abstract
As a result of tobacco samples collection research, the 25 samples or 16.7 % of the studied material were included to the variety type Virginia. The optimum plant height (153-187 cm) was shown in 5 samples: 50 Varmor, Virginia 401, Virginia 15, C - Canada, White Gold. The best in leaves size (length 38 - 50 cm, width 18-25 cm) were also 5 samples: Varmor 50, Virginia 401, Virginia 15, C - Canada, Hevesy H – 6. It was identified 5 samples by the biometric indicators: Varmor 50, Virginia 401, Virginia 15, Hevesy H – 6, Many-sheeted 55. It was found 3 varieties that exceed the standard by 0.01 t/ha (Vamor 5), 0.25 t/ha (Virginia 15) and 0.36 t/ha (Virginia 401) by the raw materials yield level. Their yield level in the three-years average was 2.21, 2.45 and 2.56 t/ha respectively and the average yield of standard variety Virginia 27 was 2.2 t/ha. The Virginia 401 and Vamor 50 varieties were characterized by high quality. The average output of higher commodity varieties was 85 % and 84 % respectively that is 5 % and 4 % higher than the standard variety data. The Virginia 84 (81 %) and Virginia 15 (81 %) varieties data were 1 % higher than standard variety data by the raw material quality. It was revealed tobacco varieties that were connected by valuable economic and biological characteristics. So, the varieties Virginia 15, Virginia 401 and Vamor 50 combined high yield with the leaves size, biometric indicators and quality. The 25 tobacco varieties, which were identified among the 150 collection samples and were match to Virginia sort type performance, were studied by breeding value. Their characteristic was given and the collection of Ukrainian Plant Genetic Resources Bank was replenished with valuable sources. The best of identified varieties were recommended for using in tobacco breeding programs as original material for the new varieties creation in the Transnistrian region of Ukraine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".