Инновационное развитие сельского хозяйства: проблемы и перспективы
Bibliographic record
Abstract
For Russia to have a weak agriculture unaffordable luxury. In agriculture busy 1/10 part of the working-age population, and that more than seven million people. A large part of the arable land on the planet are located in Russia, and starts agriculture, as we know from the earth, from how modern societies can this wealth will manage the future of our vast country. The total amount of manufactured goods account for more than 80 billion dollars a year, which greatly exceeds the performance of such countries as Argentina, Mexico, Canada and Australia. The first place we occupy in the cultivation of traditional crops oats, barley and rye. The maximum yields of these crops in the entire history of falls in the season of 2008-2009. For example, rye collected 4.5 million tons. In subsequent years, yield volumes declined slightly. On cultivation, collection and export of wheat Russia stably retains third place in the world. For comparison we collect 40-60 million tons in India 80 million tons in China 115 million tons per year. The crap we are confident leader, collecting 800 thousand tons per year since 2000-ies. On sugar beet and sunflower we are world leaders. However, sunflower oil production in 2012, we dropped to second place with a volume of 3.5 million tons, yielding Ukrainian producers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".