Comparison and Analysis of Main Management Systems of State-owned Forest in the World
Bibliographic record
Abstract
The state-owned forest is always an important part of the world's forest resources,and the public forest land accounts for 84.4% of the total forest land in the world,and the state-owned forest takes 62.36% of the total in the main developed countries including USA,Canada,Germany,UK.Australia,Japan and Russia.There are mainly four types of management systems in the world including the vertical manage system by center government,the vertical manage system by state government,the classification coordination system with focus of province,the divided management system between management and detailed work about state-owned forest management system in the world.After analysing the system form,organization construction,and design of manager and financing of abovementioned forest management systems by comparison,it is found that the all management systems are based on the property right,the consistency between the property right and management right,and use the market measures in detailed management actives;the special institution to manage the state-owned forest is constituted and has the specific administration right and responsibility in the national law;they create the official system to manage the state-owned forest and the workers who are engaged in the detailed management activity are managed as corporation's workers;the total management fee comes from the government budget and the total income are handed on to the government about the state-owned forest.Finally,the paper summarizes the experiences including ecological priority principle,classification management,administrating state-owned forest resources by government and using the market measures in the detailed management actives,and creating the completed legal system.There is important revelation to state-owned forest management system reform in China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".