Analysis of soybean world collection in conditions of south-eastern Kazakhstan
Bibliographic record
Abstract
The study of the phenotypic variability of the world crop collections in various conditions is an important step for identification of genetic factors (genes, quantitative trait loci), associated with yield and its components in order to increase the effectiveness of genetic and breeding programs.Current paper presents some results from the comparative analysis of the phenotypic data from the soybean world collection (originators -Kazakhstan, Russia, Canada, France, Sweden, Switzerland, Serbia, Belarus, Poland, Ukraine, Moldavia, Bulgaria, Belgium, Czech Republic, Slovakia, USA, China, Japan, Uzbekistan, Kyrgyzstan).The collection consisted of 192 cultivars and lines grown on the experimental plots of the Kazakh Research Institute of Agriculture and Plant Growing (KAZNIIZR, Almalybak v., Almaty region, Kazakhstan) in 2017 and 2018 yy.A number of key yield-associated traits, including plant height, pod insertion height, number of branches, nodules and seeds per plant, yield per plant and thousand seed weight were studied.It was noted that the yield over the two years of trials positively correlated with the abovementioned traits.The soybean accessions were ranked based on all the studied traits.As a result of the study of the yield components, several high-productive cultivars were identified.Cheremosh (Ukraine), Agassiz (USA), Iskra (Kazakhstan) and Evrika (Kazakhstan) showed high stable results in both years.The obtained results will be used in the genome-wide association study to identify the significant relations between DNA markers and complex quantitative traits to be applied in further genetic and breeding programs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".