Trends in Chinese paper producers and trade
Bibliographic record
Abstract
The Trends in Chinese paper producers and trade is a comprehensive analysis on the Chinese paper industry developments, and the policy driving them. There will be two primary parts: wood pulps and paper and paperboard. Wood pulps will be divided to mechanical, chemical and dissolving grade pulps. Paper and paperboard will include printing and writing papers, packaging materials, household and sanitary papers. Because of the recent rapid development of China, and improvement in the standard of living in China, Sanitary and household paper offer a huge potential market in China. There are the excessive production of high grades writing and printing paper in China, which forces producers to seek more export opportunities to sell their products. The Chinese market demand in Wrapping and package paperboard changed substantially from 1961 to 2011. As the consumption of packaging paperboard indirectly reflects the levels of social development and economy, during the period where the Chinese GDP per capita will increase from 3,000 USD to 11,000 USD, the average consumption should also increase stably and reach 100kg in 2016, and finally reach 150kg in 2020. Mechanical wood pulp is obtained by grinding or refining residues into fibers, the technology for producing mechanical pulp for printing paper has not changed much since 1980. Most improvements in technology have been in screening operations. Dissolving wood pulp is made from wood of special quality, with more than 95% alpha-cellulose content and the production is limited by the resource. Since cotton farmland has not increased and the production will not change much in the future, choice of raw material is limited to wood and bamboo. China consumes a substantial amount of chemical pulp. Canadian wood pulp has been used for a longest time in China since it has consistent quality while there are some new competitors. Brazil and Chile have been producing kraft pulp at increasing rates with the lowest cost in the world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".