Results of a study of soybean source material for breeding purposes under the conditions of Primorsky Territory
Bibliographic record
Abstract
Background. A study of the soybean gene pool adapted to the conditions of Primorsky Territory in search of useful agronomic traits is essential for further use in breeding programs. Materials and methods. Soybean accessions from the germplasm collection were tested in 2019–2021 at the Federal Scientific Center of Agricultural Biotechnology of the Far East named after A.K. Chaika. The study included 213 accessions of various origin. Cv. ‘Primorskaya 4’ served as the reference. An objective assessment of the potential of the said research material was made. Results. Three years of experiments resulted in selecting promising soybean genotypes with a set of important agronomic traits for breeding programs. Compared to the reference ‘Primorskaya 4’, an increase of more than 35% in productivity was observed in the cultivars ‘Mestnaya’ (Russia), ‘Jilin’ (China), ‘Montreal’ (France), and ‘XP 977-1.9’ (USA). Cvs. ‘No. 075-2’ (USA), ‘K0152’ (Ukraine), ‘Muzanze Stamm M 4789/74’, ‘SOJA 1065’ and ‘Adsoi’ (Germany) were characterized by earliness (100 days). Cvs. ‘Mestnaya’ and ‘HS Atlas’ may be interesting for breeders due to their highest oil content: 25.9% and 26.0%, respectively. The highest protein content was found in cvs. ‘Zhuravushka’ (39.2%), ‘XN 4’ (41.9%), ‘Torlitsa’ (41.9%) and ‘XP 977-1.9’ (39.5%). Cvs. ‘Pi 6D 4182’, ‘XN 4’, ‘Skelya’ and ‘HS Atlas’ manifested resistance to Septoria brown spot. The results of the assessment for adaptability potential showed that the following cultivars of different origin had the highest resistance to environmental stresses: ‘Primorskaya 4’ (–2.5), ‘Torlitsa’ (–2.0) and ‘Kassidi’ (–3.0).
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".