How Industries and Cities Are Seizing the Opportunity of the Bioeconomy to Enable Prosperous and Sustainable Regions: Cases from Quebec
Bibliographic record
Abstract
Quebec, a province in Canada, is well positioned in the global bioeconomy. Its regions are overflowing with forest, agricultural crops or other organic residues that can be recovered and converted into bioproducts and bioenergy. Quebec's strength has long been in the forest products industry and municipal solid waste recycling. Product diversification is now targeted by many companies and municipalities. Value chains for bioproducts and bioenergy are set in practically all of Quebec's regions. In fact, most of them have their own “community-scale” bioeconomy project, even if the province of Quebec itself does not have yet its own bioproducts or bioeconomy roadmap. In this paper, various community-scale bioeconomy projects are presented and discussed. The role of cities and other local stakeholders in the deployment of these projects and the focus on getting products or coproducts for local uses are also elaborated. A framework involving the positive involvement of national and regional institutions and the development of a network with local stakeholders is proposed to increase the chance of success of community-scale bioeconomy projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".