Assessing interstate softwood roundwood trade in the southern United States: a gravity trade model approach
Bibliographic record
Abstract
Intraregional trade of forest products is a critical component of regional and subregional timber markets in terms of supply chain planning and locating forest product manufacturing facilities. Based on a gravity trade model, we evaluated the various factors driving the interstate flows of softwood sawlog and roundwood pulpwood in the 13 southern US states: AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX, and VA. Biennial state-level panel data from 2011 to 2019 in 13 southern states were employed to estimate empirical sawlog and pulpwood trade models. The results suggest that state gross domestic product (GDP) of importing states, exporter and importer production, importer consumption, the distance between the trading partners, and the electronic logging device mandate are influential factors of softwood sawlog trade between the states. Similarly, state GDP, exporter pulpwood production, importer consumption, the distance between the trading partners, delivered timber prices, and pellet mill capacity in each state are found to be significant determinants of softwood pulpwood trade between the partners across the state borderlines. The findings provide forest managers and policy makers with additional insights on the growing bilateral timber trade dynamics in regional and subregional markets in the southern United States.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".