Статистические исследования мирового производства зерна ячменя
Bibliographic record
Abstract
The article presents an analysis of the statistical data on use of barley in the world agriculture. The dynamics of changes in acreage under crop has been studied over the past decade in the global community. The structure of indicators in barley’s area was reduced and the main regions of production have been described. As the world as some countries barley productivity was studied. The article presents the data on world production of barley for lust four years. The structure of production of the main producing counties for 2014 is presented graphically. On the basis of the material revealed, more than half of the total harvest of barley in the world has been produced by three regions: The European Union, the Russian Federation and Canada. The article presents a comparative analysis in the above productivity of barley by the world leaders in the production of this crop. A direct influence of environmental factors on final grain yield has noted. Also the issue of national economic importance of culture has been revealed. We have noted the areas of the culture use such as food, brewing industry, as well as livestock (as it is known, barley is one of the most valuable forage crops)
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".