Strategic planning in the forest sector developed timber-producing countries as a key tool for the rational use of natural resources
Bibliographic record
Abstract
The problem of rational use of resources is a priority for all mankind, which is confirmed by the presence of many national and interstate programs in the field of environmental management, resource conservation and improving the efficiency of environmental management. Forest complex play a special role in the rational use of natural resources. Forestry has a long process of reproduction, so a set of measures for the use, safety, integrity, reproduction of forests and the balance of the forest resources market conjuncture is necessary. A long-term national strategy is needed to achieve sustainable economic growth. Canada, Sweden, Finland, Latvia, Belarus, and China have national strategies for the forest sector development on 10-20-50 years. The strategic planning process in the forest sector is ambiguous and controversial; it includes the coordination of different interests of actors and economic-mathematical modelling, assessment of the effectiveness of environmental management and resource management. The research objective: strategic planning analysis in the above-mentioned countries forest complex in the context of natural resources rational using. The following factors were identified: the sequence of making economically significant decisions by the State; the strengthening role of the public in the decision-making process on the forest resources rational using.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".