World oat production: problems and development trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it