Modeling bilateral forest products trade.
Bibliographic record
Abstract
Abstract The focus in this chapter is on the development of mathematical programming models used to model bilateral forest products trade. Theoretical outlines are provided of a multi-region, single product trade model and of an integrated, multi-region, multi-product trade model. The objective function and constraints are described mathematically, while the analysis takes into account horizontal and vertical chains and the need to calibrate the model using observed trade flows. Data sources are discussed, and the GAMS code is provided for the uncalibrated and calibrated versions of the model. The Canada-U.S. softwood lumber dispute is the raison d'être for much applied work in modeling forest products trade, especially on Canada's side. In this chapter, we examine several spatial price equilibrium (SPE) trade models that are currently used to investigate the implications of trade barriers imposed on Canadian exports of softwood lumber to the United States. The reason we consider bilateral trade is so that we can determine the impacts of trade restrictions on various regions in North America. We begin in the next section by specifying a general but vertically integrated SPE trade model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".