World oat production: problems and development trends
Bibliographic record
Abstract
The study provides an analytical review of global oat production dynamics from 1992 to 2022 based on statistical data from the Food and Agriculture Organization of the United Nations. Research materials and methods: The theoretical basis of the study was the works of famous scientists directly affecting various aspects of global oat production. The methodological basis of the study was the following methods: comparison, time analysis, systematization of data. The empirical basis of the study was statistical data from the Food and Agriculture Organization of the United Nations. The statistical database of the Food and Agriculture Organization of the United Nations was accessed through a program written in the Python 3.12.3 programming language and executed on the ipykernel core. To work with the data and visualize them, which are reflected in the article, the pandas 2.2.2, plotly 5.22.0 and ipywidgets 8.1.2 libraries were used. and the IPython.display module. Research results. Canada and the Russian Federation produce almost 40 percent of the world's oats. There is a steady trend towards a decrease in oats volumes on the world market for this agricultural crop. Most leading countries are reducing the area under this crop and its production volumes. There are a number of countries that are increasing their development potential on the oats market (increasing production volumes and increasing the area under crops): Spain, Brazil, Great Britain, Canada. These countries are increasing the volume of storage areas for this crop and increasing the efficiency of these areas due to the increased oat harvest per 1 ha.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".