US Proposed Tariff: A glance at the Wood Sector.
Bibliographic record
Abstract
The global trading system is facing unprecedented strain. The resilience and foundational principles of the trading system are being tested. This study analyzes tariff announcements made in the lead-up to the current administration. These announcements outlined plans to impose a 25% tariff on imports from Canada and Mexico and 10% tariff on all U.S. imports, aiming to strengthen domestic industries and rectify trade imbalances. This tariff plans are expected to impact U.S. wood sector which is closely integrated with the Canadian and Mexican markets. Two scenarios were modeled using the FOrest Trade Equilibrium Model (FOTEM), which utilizes a general equilibrium model framework. The first scenario considered the U.S. imposing tariffs without retaliation from Canada and Mexico, and the second scenario considers a retaliatory measure by Canada and Mexico. The results show varying impacts across countries. Notably, while Canada and Mexico face substantial declines in GDP and sector outputs, the U.S. economy appears relatively insulated, with minimal impacts on GDP. Most of the U.S. sectoral output declined after retaliatory measures by Canada and Mexico, but the impact of the tariffs remains minimal. When the dollar value of the wood sector is aggregated and considered, a retaliatory tariff on the U.S. wood sector tends to severely worsen the overall U.S. wood output. Significant decline in import volumes was observed and the potential for retaliatory tariffs could disrupt the intricate interdependencies that define North American trade. The tariff policies are anticipated to increase production costs, disrupt supply chains, and negatively affect wood-dependent sectors, particularly the U.S. housing industry. A balanced approach that promotes domestic growth while mitigating adverse effects on trade partners may yield more favorable outcomes for all stakeholders in the wood sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".