A review of Canadian wood conversion technologies for the production of fuels and chemicals
Bibliographic record
Abstract
Abstract Canada has 347 million ha of forest cover, contributing to the potential large availability of wood‐based resources. Although Canada's forest sector contributed $23.7 billion to the national nominal gross domestic product (GDP) in 2019, the GDP contribution of the wood product manufacturing subsector shrank by 6%. To reposition the Canadian forest industry, new forest management practices and wood‐based conversion technologies should be applied. In this context, the use of woody biomass in biorefineries to produce clean energy, fuels, and chemicals is becoming increasingly significant. There is a need to understand the current status and challenges of the wood‐based biomass conversion technologies that have been and are being developed in Canada. This information will help decision‐makers in formulating and implementing forest sector‐related policies for a sustainable bioeconomy in Canada. This study is focused on a review of Canadian woody biomass conversion technologies. Our critical review identified considerable potential biomass conversion technologies specialized for woody feedstock, all in the Canadian setting. We focused on the prospects of revitalizing Canada's pulp and paper industry through the integration of pre‐treatment processes and biochemical technologies. The thermochemical conversion pathway was identified as the dominant route for woody feedstock valorization. The review also identified pathways with the potential to diversify the existing product mix that generate products from wood streams, such as chemicals and biomaterials. Most of the biochemical and thermochemical research done in institutional and multi‐institutional research collaborations from laboratory scale to industrial scale will boost the chances of the commercialization of a wood‐based biorefinery in Canada.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".