Bibliographic record
Abstract
Abstract We assessed the prospects of lentil cultivation in the Russian Federation and the most favorable regions for this. The following tasks were set: we assessed the importance of lentils in the country’s economy, identified promising regions for growing lentils, taking into account agronomic and economic conditions. When analyzing suitable regions for lentil cultivation, not only the agrotechnical conditions of cultivation were taken into account, but also economic factors, for example, the proximity and volume of sales markets, including exports. The selection of promising regions for growing lentils was made on the basis of its agrobiological properties, existing cultivation volumes and agro-climatic conditions of the regions of the Russian Federation. The impact of global climate change and the dynamics of lentil cultivation volumes in recent years were taken into account. Canada, as one of the world leaders in growing lentils, is located at the same latitude with the regions of the Saratov and Volgograd regions. In Russia, the Saratov and Volgograd regions are in good soil and climatic conditions for growing lentils. The high gross harvest was the result of an increase in the acreage under lentils, the value of which in 2019 amounted to 274 thousand hectares, which is 3 thousand hectares more than last year. The production of lentils is going on with a noticeable increase, which is due to the significant orientation of the cultivation of this crop for export. According to the AB-center, in 2015, export deliveries of lentils amounted to 7.4 thousand tons; in 2016-17.2; in 2017-64.6 thousand tons, 2018-77.9 thousand tons; 2019 – 79.8 thousand tons. In the course of research, it was found out that lentils play an important role in the national economy of the country. It is determined that the regions of the Saratov and Volgograd regions are the most promising for expanding lentil production both in terms of agro-climatic conditions and economic potential.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".