DEVELOPMENT OF A DEVICE FOR MILLING TILLAGE WITH SIMULTANEOUS APPLICATION OF FERTILIZERS
Bibliographic record
Abstract
In all developed countries of the world there is a search for new technological methods of tillage, aimed at protecting it from erosion, preserving and increasing fertility, reducing costs. The main means that can prevent the loss of soil fertility is the introduction of energy-saving and fertility-saving technologies. The factors of soil trauma and the maximum coverage of the soil with plant residues are laid down. The most well-known technologies are technologies that are quite common in the United States and Canada, where more than 50% of the land is cultivated, as well as in Argentina, where they account for more than 50%. In Europe, countries such as Germany, France and the United Kingdom will cultivate about 10% of the land, and in Ukraine - only about 5%. The functionality of machines is expanding, the design is becoming more complicated, their weight is increasing. In all developed countries of the world there is a search for new technological methods of tillage, aimed at protecting it from erosion, preserving and increasing fertility, reducing costs. Various methods of minimizing tillage are widely implemented, as well as the partial replacement of dump plowing with dumpless tillage, research and practical actions aimed at finding effective, alternative, modern tillage technologies to maintain and increase the fertility of the land, which articles. Thus, in order to preserve the soil fertility of the lands of Ukraine, theoretical studies of modern tillage technologies were conducted and the authors proposed the design features of the device for strip or continuous tillage with simultaneous application of fertilizers prior to design and design, the main results of which are presented in articles.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".