Eco-Economic Systems of Russian Agriculture: Statistical Analysis
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
Over the recent 25 years Russia has experienced economic transformations that significantly have changed the impact of business activities on the state, structure, quality and use of natural resources: an annual decrease in the sizes of agriculturally used areas, reduction of soil fertility, destruction of the amelioration system, etc. Certainly, all this affects the economy and makes it necessary to investigate these interrelated processes in the context of an ecological-economic system. The article presents a multilateral statistical analysis of the eco-economic system of Russia, offers recommendations for improvement of the indicator system, analytical methodologies as well as proposals for implementation of the international standards into Russian statistics. It is emphasized that the asset value accounting philosophy should be developed on the basis of the specific nature of Russian statistics with application of the international practices. The article offers to start developing the macroeconomic accounts from the municipal level and then to aggregate the information at the level of a region and the country.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it