Инновационное развитие сельского хозяйства: проблемы и перспективы
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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 it