Experience of growing soybeans (<i>Glycine max (L) merryll</i>) on irrigation in the unstable moisture zone of the Stavropol Territory
Bibliographic record
Abstract
To obtain a stable harvest of high quality grain, Agrosakhar LLC, located in the Stavropol Territory, used soybean growing technology, which included: the use of modern energy and resource-saving equipment for soil cultivation, sowing and harvesting, cultivation of adapted varieties bred in Russia and Canada - Selecta 302, Vilana, Furio, Kofu, Kyoto, Kanata; introduction of complex fertilizer - azophoska for main soil cultivation, pre-sowing seed treatment with a fungicidal dressing agent Delit Pro and the inoculant Highcoat Super Soy. The system of protective measures included a combination of agrotechnical measures using chemical plant protection products based on monitoring of harmful objects. To combat monocotyledonous and dicotyledonous species of weeds, sowing was treated with Pledge herbicide before germination, followed by a tank mixture of herbicides Bazagran with Harmony in the phase of the first true leaf in soybean plants. The use of the fungicide Akanto Plus together with Karate Zeon and Ampligo Plus ensured effective protection of soybean plants from diseases and pests during the growing season. The technology used for growing soybeans on the farm enables you to consistently get a large and high-quality grain yield. The maximum yield of 2.92 t/ha was obtained by sowing the Kofu variety using the developed cultivation technology. On average, the yield of protein amounted to 0.98, and vegetable fat amounted to 0.59 t/ha. The profitability of soybean grain production on the farm using this cultivation technology is 44.2%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".