Modeling Forest Trade in Logs and Lumber: Qualitative and Quantitative Analysis
Bibliographic record
Abstract
This paper deals with forest trade modelling from a theoretical, analytic and empirical perspective. An integrated dynamic log-lumber trade model is developed and then used to examine two trade issues, namely, a reduction of Russian taxes on log exports and removal of the taxes on Canadian lumber destined for the United States. To demonstrate the dynamic aspect of the model, both sets of taxes are lowered over a period of time. The trade model consists of five Canadian regions, three U.S. regions, New Zealand, Australia, Chile, Rest of Latin America, Russia, Sweden, Finland, Rest of Europe, Japan, China, Rest of Asia, and Rest of the World – a total of 20 regions. It concerns only coniferous logs and softwood lumber, ignoring hardwoods. The model is also calibrated on 2010 observed bi-lateral flows of logs and lumber using positive mathematical programming. The forest trade model is written using an Excel-GAMS interface, with input data retrieved by GAMS from Excel and GAMS output written to Excel, where final calculations are made.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".