Spring barley is a promising crop in agricultural production
Bibliographic record
Abstract
Spring barley occupies an important place in modern agriculture, offering both economic and environmental benefits, which confirms its importance in the context of food security and sustainable development of the agricultural sector. In 2023, the gross harvest of spring barley reached 27 million tons globally, where the largest producers are countries such as Canada, Australia, and Germany, providing high yields thanks to modern agronomic practices and optimal climatic conditions. In Russia, the average yield of spring barley varies from 2.4 to 3.0 t/ha, with a gross harvest of about 5.5 million tons, and the most significant volumes are obtained in the Krasnodar and Stavropol territories. The prospects for growing spring barley look promising due to its adaptability to changing climatic conditions and its ability to effectively use minimal resources. Spring barley is also a valuable source of proteins and carbohydrates, which makes it irreplaceable in the diet for both humans and farm animals. In general, spring barley is a promising crop for development both in Russia and in the world, and its cultivation will contribute to improving food security and sustainability of agriculture in face of global changes.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".