The competitiveness of Canadian softwood lumber: a disaggregated trade-flow analysis
Bibliographic record
Abstract
A recursive dynamic spatial equilibrium model is used to examine the global competitiveness of Canadian softwood lumber. To address the restrictive assumption of softwood lumber homogeneity, this study disaggregates softwood lumber into two product groups: (i) higher grade lumber that includes appearance, select structural-grade lumber, and Japanese J-grade lumber; and (ii) lower grade lumber that includes the United States dimension lumber that is commonly used in construction and utility- and economy-grade lumber. Factors that may affect global softwood lumber markets are simulated in the model to project global softwood lumber trade flows from 2012 to 2021. The results indicate that the reduced lumber supply in western Canada caused by the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation combined with demand increases in several regions of the world will contribute to a global increase in softwood lumber prices. Our results suggest that the global price increase will be greater for lower grade softwood lumber than for higher grade lumber. The United States and China will continue to be the top two markets for lower grade Canadian softwood lumber. Although Canadian exports of lower grade softwood lumber to the United States are expected to increase marginally over time in response to the recovery of American housing starts, softwood lumber exports to China are expected to drop significantly, and it is forecasted that exports from the Russian Federation will fill that void. These findings provide strong market signals for both forest managers and the forest-products industry to assess supply chain profitability and adjust production planning accordingly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".