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 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.
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.
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".