Appalachian Hardwood Product Exports: An Analysis of the Current Chinese Market
Bibliographic record
Abstract
A mail survey of Appalachian hardwood product exporters was conducted in the fall of 2008 to analyze the export practices for Appalachian hardwood products, specifically the volume of hardwood products exported to the Chinese market, their preferred species, and potential and existing trade barriers between US producers and Chinese customers. Results of the survey showed that the most frequent export destinations of Appalachian hardwood products were Europe, China, Canada, Mexico, and Japan. In 2007, approximately 11.4 million board feet (MMBF, Doyle scale) of hardwood logs and 145.3 MMBF of hardwood lumber were exported to China by the respondents. Approximately 37 percent of the respondents who exported hardwood products to China exported red oak logs, followed by white oak, black walnut, black cherry, and hard (sugar) maple. The top species of hardwood lumber exported to China were red oak, white oak, yellow poplar, black walnut, hickory, cherry, hard maple, and soft maple. Respondents indicated that transportation freight costs and payments are the limiting factors when considering expanding business overseas. The continued decreasing hardwood price has put more pressure on hardwood products exporters to maintain profit margins. Because of the current economic downturn, hardwood production in the Appalachian hardwood region has declined by more than 40 percent. Exports of hardwood products to China will be affected to some extent. However, it is expected that China will remain an important overseas market in the near future.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".