Сорта сои, рекомендуемые для использования в селекции по программе импортозамещения
Bibliographic record
Abstract
The main objective of the work was to investigate and save of the soybean genetic potential to identify donors and sources of valuable breeding traits. The study of samples in the fields of the branch of the Kuban experimental station of VIR was conducted from 2008 to 2015 in accordance with the methodological guidelines of VIR. The cultivar Komsomolka (VNIIMK) was used as a standard. Traits of the standard cultivar had the following average meanings: yield was 313.7 g per m2, seed yield per plant -39.9 g, 1000 seeds weight 169 g, the average height of the lowest pod attachment 10 cm. there were studied 349 new soybean samples. The main valuable economic and breeding traits are studied. Some collection soybean samples are very interested for breeders: on early maturity i-611486 (Sweden) with the vegetation period 85 days; on high seed yield per plant i-614237 (Switzerland), i-614235 (France), i-614244 (Canada), i-0144391 (Russia); on yield -i-0144391 (Russia), i-614076 Lara (Serbia); on 1000 seeds weight i-606924 (Canada); i-606933 (China) (1000 seed weight was 337 and 234 g, respectively). Samples i-606951, i-606942, i-606933 (China) are distinguished by the height of the lowest pod attachment 26; 23 and 20 cm, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".