Bibliographic record
Abstract
International trade in forest products has continually increased in recent years, especially within North America, West Europe and Pacific Rim regions. Many firms are getting large profit by trading forest products in such circumstance, and expand their business as well. This paper aims to clarify the business activities and growth of big wood-based firms in the United States and Canada. These companies expand their business activities mainly through merger and acquisition (M & A) of existing firms as well as investment to new facilities based on internal accumulation. M & A occurred mostly within paper and allied industry, and lumber and wood products industry. Assets accumulated by such big firms are substantial. Several giant wood-based firms in North America, especially in the United States, have developed diversified management system of wood-based business and formed multinational corporation so far. However, at the same time, they make a keen competition between themselves and big firms in the other regions of the world.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.066 |
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; both teacher heads agree on what is shown here.
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".