Assessment of soybean raw material in regard to plant adaptability in the climatic conditions of the Middle Volga Region
Bibliographic record
Abstract
Abstract The ecological flexibility, stability, and adaptability of the “seed weight per plant” trait of soybean raw materialwere assessed in the forest-steppe conditions of the Middle Volga Region. The most valuable varieties for use in the breeding process for this trait were selected. The experimental work was carried out in 2019—2021. In total, 14 soybean varieties of various ecological and geographical origins were assessed. The regionalized agricultural variety of local selection, Yuzhanka, was set as the standard. The soil at the experimental plot was represented by typical medium-humus, medium-thick, medium-clay chernozem. The humus content was 5.8—6.9%, the content of mobile forms of phosphorus, 133.6—156.5 mg/kg, exchangeable potassium, 154.0—180.0 mg/kg. In 2019—2021, in the forest-steppe of the Middle Volga Region, average individual productivity of soybean plants was 7.47—12.17 g. The highest individual seed productivity was registered in the varieties 680-11 (Ukraine), 422 (Kazakhstan), OX 299 (Canada), L-60/2018 and Nika (Russia), exceeding the standard by 4.97—5.43 g. The lowest total rank was found for varieties 422 (Kazakhstan) – 23, Nika (Russia) – 28, Gessener (Yugoslavia) – 28, L-59/2018 (Russia) – 29 , OX 299 (Canada) – 31, and 680-11 (Ukraine) – 34, while the standard variety refers to 72. These varieties are promising raw material for developing soybean varieties with high productivity potential due to the effective use of the soil and climatic conditions of the region and resistance to dominant environmental stress factors.
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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 | 0.001 |
| 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) | 0.001 | 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 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".