STATE AND PROSPECTS OF VEGETABLE GROWING DEVELOPMENT IN SIBERIAN FEDERAL DISTRICT
Bibliographic record
Abstract
The paper considers the current state of the vegetable growing industry in Siberian Federal District over last quarter of a century. In the Siberian Federal District, 1.5 million tons of vegetables are produced annually, and according to rational food standards, 2.8-3 million tons are needed for the district population, i.e. 1.5 million tons should be brought in. Vegetable producing was rather stable in Siberian Federal District over 1990–2015. Novosibirsk, Omsk and Kemerovo regions and Altai and Krasnoyarsk territories are the main district suppliers of vegetable production for their own needs and other regions as well. Republics of Altai and Tuva do not produce vegetable cultures due to unfavorable nature-climatic conditions; they take the 11th and 12th places among Siberian regions. On the database of the Federal State Statistic Service over 25 years, there was analyzed use of land sown to vegetables, dynamics of vegetables croppage and productivity in the Russia Federation and Siberian Federal District. Vegetable crops productivity was influenced by advanced technologies of vegetable growing on covered soil such as hydroponics, drip irrigation and vertical vegetable growing. In modern greenhouse enterprises, productivity can reach up to 180 kg/m2. A series of factors limiting vegetable growing development is pointed out. Indicators of the industry development up to 2025 are calculated and measures for their achievement are suggested.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".