Bibliographic record
Abstract
Forestry in China has changed drastically since the country was affected by devastating floods in 1998.The government has launched a series of nationwide ecological restoration programs, promulgated new forest policies and tax reforms, and heavily compensated forest owners.These policies and programs have already produced tangible benefits in improving forest cover, supporting the wood industry and supplementing rural livelihoods.Large areas are now protected from logging, huge afforestation programs are underway, and tenure reform offers hope of more efficient and effective operations that can create jobs and stimulate economic growth [1] .However, forestry has also been associated with problems, particularly in the context of climate change and the expansion of urbanization, including deforestation, desertification, pest and disease outbreaks, and decline of productivity.Despite this, these challenges also represent opportunities for China.Forest conservation programs have generated a wave of new national parks and ecotourism businesses; afforestation and reforestation programs have improved forest genetic resources and have also led to the development of carbon forestry.The natural forest protection program has encouraged the use of biomass and massive non-timber understory crop plantations.This special issue organized by the Chinese Academy of Forestry provides a good summary of the multifaceted nature of Chinese forestry today.The issue highlights ecological conservation programs in Chinese forests, from concept to practice (Shen, this issue), and the experience gained and lessons learnt from combating desertification over the last thirty years in China are of great potential benefit for the rest of world (Bao et al., this issue).The establishment of a national forest certification system, a market-based instrument for promoting sustainable forest management, has become a vitally important policy tool for China, particularly when non-timber forest products are major sources of income for local communities (Lu & Muthoo, this issue).As a crucial source of fiber, Eucalypt plantations have become the backbone of the Chinese forest industry, including seedling propagation, fertilizer production, lumber, pulp and paper, and wood-based panels, as well as bioenergy production (Xie et al., this issue).A key area of forest research has been genetic improvement and clonal selection; both have contributed to substantial improvement in forest productivity.The special issue also highlights research progress with Betula and Larix (Yang, Sun et al., this issue).Another study examines the modeling of climate change at a local operational scale in the Asia Pacific region, which has provided an opportunity for scientists, forest managers and decision makers to develop local adaptive management strategies (Wang et al, this issue).Bioenergy and biofuels, including forest residuals for wood pellets, are important energy strategies for China, and heat-and-electricity generation has been widely used in northern China.Bioenergy development in Brazil, Denmark, Sweden and the USA may provide some good lessons for China (Hu et al., this issue).The case study of regional GIS-based evaluation of the potential and supply costs of forest biomass in Sweden (Athanassiadis & Nordfjell, this issue) could provide a practical approach for China in its bioenergy decision-making.Several studies dealing with forest genomics, pest and disease control, and wood structure are also presented (Li et al., Biswas et al., Chen et al, and Li et al., this issue).All of these will help readers to gain insights into the current development of forest science and technology 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".