COMPARATIVE EVALUATION OF PRODUCTIVITY INDICATORS OF DOMESTIC AND FOREIGN SOYBEAN VARIETIES IN THE CONDITIONS OF THE ALMATY REGION
Bibliographic record
Abstract
This article presents a comparative study of six Kazakhstan-bred soybean varieties (Victory, Pamyat YGK, Lastochka, Aisaule, Zhansaya, Yelmerey) and 19 foreign varieties (Delta, Slavia, Selecta 301, Vilana, Korsak, Galina, Trijumf, Voyevodzhanka, Sava, Ascacubi, Hilario, Blamcos, Atlantic, Luna, Safrana, Santana, Sponsor, Zen, Dekabig) conducted at an ecological variety testing nursery. Among the Kazakh varieties, Yellmerey, Zhansaya, and Aisaule stood out for their high yields of 49.2 c/ha, 48.5 c/ha, and 47.3 c/ha, respectively. The top-yield foreign varieties were Voyevodzhanka from Serbia with 48.8 c/ha, Sava with 49.0 c/ha, Luna from Italy with 51.8 c/ha, and both Atlantic and Blamcos with 46.0 c/ha each. The French variety Sponsor also achieved a high yield of 51.7 c/ha. The Kazakh variety Aisaule and Italian variety Luna were notable for their high fat content at 21.9% and 22.2%, respectively. Varieties such as Victory and Aisaule from Kazakhstan, along with Delta, Selecta 301, and Vilana from Russia, Korsak from Ukraine-Canada collaboration, and Safrana from France, exhibited high protein content ranging between 38.6% and 40.6%. These outstanding varieties serve as a breeding resource for developing new, high-yield soybean varieties for the southeastern region of Kazakhstan.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".