Wood-Based Bioenergy in North America: An Overview of Current Knowledge
Bibliographic record
Abstract
Policy priorities for wood-based bioenergy in North America have undergone fluctuations over time, influenced significantly by the dynamic interplay of sociopolitical factors. Recent years, however, have seen a renewed public interest in wood-based bioenergy in the United States, Canada, and Mexico. This resurgence is driven by fluctuating energy prices and growing concerns about climate change. This review provides an overview of current energy production and consumption scenarios, and highlights critical issues related to the sustainability of bioenergy feedstocks and their economic potential across the three North American countries. Different cross-cutting issues related to public health, climate change, and social acceptance of wood-based bioenergy are thoroughly examined. Within this context, several challenges have been identified, including uncertainties in climate projections, inadequate tree inventories beyond forestlands, deforestation concerns, technological shifts in wood processing, fluctuations in bioenergy demand, and the imperative need for access to reliable markets. Addressing these challenges requires increased research and investment in wood-based energy to enhance energy security, reduce greenhouse gas emissions, and improve economic and social viability in bioenergy production. This proactive approach is vital for fostering a sustainable and resilient wood-based bioenergy sector in North America.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".