Objectives of improving forest relations in Russia, taking into account foreign experience
Bibliographic record
Abstract
Subject/topic. Our country has significant reserves of forest resources and a well-developed timber industry. Therefore, it is extremely important to creatively use the experience of regulating forest relations in states that have similar natural resources and forms of state forest management to the Russian Federation. Goals/objectives. The purpose of the study is to summarize the experience of the USA, Finland, Norway, Canada and other countries, where the interaction of the state and private business, taking into account the interests of the population, is the basis of forest legislation, to assess the possibilities of its application in the Russian Federation. Methodology. The methodological basis of the article is based on general scientific methods – synthesis, deduction and statistical methods of information processing. The materials of the official websites of state bodies in the field of forest legislation of the studied countries, as well as data from the Federal State Statistics Service (Rosstat), served as an information base. Results. The rating of states by the share of the timber industry in the structure of manufacturing industries and its contribution to the formation of industrial production is considered. Based on the analysis of state programs for the development of the timber industry, the possibility of applying specific best foreign practices for the conditions of the Russian Federation has been determined. Conclusions/significance. As a result of the study, it was determined that the existing successful forestry policy in developed countries does not give reason to assume that it will also be effective in Russia, but we believe that comparing different management strategies and practices abroad is useful in order to select them for use in our country. Application. The results of the study can be applied in practice in the development of regional timber industry development programs for the sustainable development of the forest industry.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".