PROSPECTS FOR THE USE OF SOYBEAN COLLECTION MATERIAL USED IN BREEDING STUDIES IN PRIAMURYE (AMUR REGION)
Bibliographic record
Abstract
The article presents the findings of investigations on the soybean collection material under the natural and climatic conditions of Priamurye. The experimental part of the work was carried out at the All-Russian Research Institute of Soya Laboratory of Soya Breeding from years 2008 till 2018. The weather conditions during the years of research differed in temperature and moisture conditions, but in general were favorable for soybean cultivation. Field experiments were arranged on the plots of crop rotation intended for breeding (Village of Sadovoye, Tambov district, Amur Region) on meadow chernozem-like soils. Integrated assessment was carried out for more than 500 proved varieties and specimens of soybean from the USA, Algeria, China, Canada, Germany, Romania, Serbia, France, Italy, Japan, Sweden, Poland, Hungary, Austria, Switzerland, Czech Republic, Yugoslavia, former Soviet republics - Ukraine, Moldova, Belorussia and scientific research institutions of the Russian Federation. All varieties and specimens of soybean were studied for 3-5 years, in order to identify sources of economically useful features for their further inclusion in the breeding process. The researches revealed the most promising varieties of the world collection intended for use as starting material (base line) in practical crop breeding. The best varieties were determined in accordance with their usage as follows: ultra early-ripening, early-ripening, highly productive and resistant to the pathogens, prevalent in the region, high-protein forms, etc. More than 320 different combinative crossbreedings were carried out; obtained hybrid progeny in 311 combinations which was successively studied according to the full scheme of the selection process. The hybrid material selected in accordance with various directions of breeding research makes it possible to create highly productive soybean varieties of a new generation, various maturity groups with improved economically useful features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".