Studying Imports of Wood, Paper and their Products in the World and Iran from 1998 to 2007
Bibliographic record
Abstract
Important importers of wood, paper and their products were identified in this study. Identification of these countries is important for the trade of wood and paper products. The main questions now are: how is imports situation of wood, paper and their products in the world? And which countries are important here? Average values and percentages of imports were calculated to rank regions on the basis of FAO stats from 1998 to 2007. The annual rates of change in imports were calculated by single payment formula and the positive correlation of import prices was determined by coefficient of determination. Our results showed that imports of products frequently had increasing trends in the study period, and they had stable markets with low fluctuations. China and Germany were the most important countries with France, Italy, Japan, Canada, Belgium, Holland, Austria, South Korea, Mexico and Denmark ranking next for these products. Wood and paper products of Asian countries were developing faster than other continents. Import efficiency of paper and wood products of Iran is low due to their high added value. Political developments after 2001 and increasing energy prices resulted in increase in products price worldwide with trends being increasing with high coefficients of determination (R2).
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".