An Analysis of U.S. Hardwood Log Exports from 1990 to 2021
Bibliographic record
Abstract
Abstract In 1990, the major destinations for hardwood logs exported by the United Sates were Europe, Canada, and three East Asian markets: Japan, Taiwan, and Korea. From 1990 to 2005, the volume of hardwood logs exported to Canada increased by 402 percent. During this period, another East Asian log market developed, consisting of China, Hong Kong, and Vietnam (CHV). While increased Canadian exports were an apparent result of increased U.S. bilateral trade with Canada, the development of the CHV market was associated with increased U.S. furniture imports from that region. The volume of U.S. log exports worldwide peaked in 2005, and the value of log exports peaked in 2007. Exports to all regions declined in 2009. After 2009, exports to CHV increased and surpassed shipments to Canada in 2014. In the past decade, much of the increase in exports to CHV appears to be the result of demand within China. Recently, these exports have been affected by trade disputes and the COVID-19 pandemic. For most of the study period, the dominant log export species were white oak, red oak, maple, or cherry in terms of value. Since 2018, walnut has become the most important log export species (value basis) as a result of increased shipments to China.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".