Вихідний матеріал для селекції технологічних сортів сочевиці
Bibliographic record
Abstract
Materials and methods. As of January 1, 2021, the National Center for Plant Genetic Resources of Ukraine has a collection comprising 1,101 lentil specimens from 54 countries, including 90 Ukrainian specimens. Of these, 17% are breeding varieties, 45% are breeding lines, 37% are trivially bred varieties and forms, and about 1% are five wild lentil species. The collection specimens were evaluated in accordance with valid methods. Our objective was to evaluate the collection for the traits that determine ease of production and to offer starting material for breeding. Our purpose was to identify sources of valuable economic characteristics for the breeding of easily producible lentil varieties. Results and discussion. Basing on the multi-year results, we selected and proposed the starting material to create easily producible lentil varieties with the following characteristics: high yields of seeds (11 macrosperma sources (Krasnohradska 5 originating from Ukraine [1000-seed weight = 73 g]; 1913 T 15 from Canada [66 g]; UD0600141 from Spain [91 g]; and others) and 25 microsperma sources (UD0600707 from Ukraine [34 g]; Miledi from Russia [38 g]; CDC Redcap from Canada [38 g]; and others)); suitability for mechanized harvesting (6 macrosperma sources (Mistseva 5 from Ukraine, Ilina from Slovakia, 1921 T 11 from Canada, and others) and 18 microsperma sources (Novourenskaya 3565 from Russia, Pozdnyaya from the Czech Republic, CDC Redwing from Canada, and others)); improved biochemical composition and high cooking qualities of seeds (3 macrosperma sources (local accessions: UD0600141 from Spain, UD0600329 from Syria, UD0600151 from Mexico) and 10 microsperma sources (local accessions: UD0600451 from Bulgaria, UD0600017 from Afghanistan, UD0600979 from Israel, and others)). Conclusions. For 30 years of intensive work of the National Center for Genetic Resources of Plants of Ukraine, divers and original staring material of lentil has been collected and studied; sources of valuable economic characteristics have been identified for breeding programs of research institutions of Ukraine and other countries
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".