The principles of improving the technology of grain crop cultivation
Bibliographic record
Abstract
Abstract The article describes issues of improving technologies for the cultivation of major agricultural crops, technical re-equipment of agricultural production particular processes, proposes new technologies, high-production technical means, methods of completing energy-rich units and ways to increase the competitiveness of crop production. The article proposes the program for the material and technical re-equipment of winter wheat production in the Krasnodar Krai. The problems of harvesting grain crops with combine harvesters are considered and the new threshing scheme by the “unwinnowed grain” method with harvesters, made in Canada, is proposed. The issues of using different varieties of wheat in terms of maturation are considered in order to increase the sowing time and further harvesting periods and, hence, reduce the number of sowing and harvesting equipment. The technology has been developed for the cultivation of winter wheat using new harvesting equipment and new methods of sowing wheat of different ripening periods. The transition to the proposed technology of winter wheat cultivation and harvesting locally in one of the Krasnodar Krai regions will lead to significant annual savings in labor costs. With the strict implementation of the technology, in particular, the optimal sowing and harvesting timing, using the new technology, the fields will be completely free of weeds over several years.
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 | 0.001 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".