The Future of Forest-based Bioeconomy Areas:Strategic openings in Uruguay and the World by 2050
Bibliographic record
Abstract
This VTT Technology report on 'The Future of Forest-based Bioeconomy Areas: Strategic openings in Uruguay and the World by 2050' is the result of a multistakeholder and multi-disciplinary foresight exercise commissioned by the Government of Uruguay in 2018 and completed in 2019. The overall goal of the project was to contribute to Uruguay's National Development Strategy 2050 through the following specific objectives: (1) To identify key global forest-based bioeconomy areas by 2050; (2) To identify needs and gaps in the prioritised FBA in Uruguay; (3) To develop a shared vision for the forest-based bioeconomy in Uruguay by 2050; and (4) To develop a strategic Action Roadmap to achieve the shared vision for the forest-based bioeconomy in Uruguay by 2050. The most important outcomes of the project include: a shared vision for key foresight-based bioeconomy areas (FBAs), as well as five consolidated Action Roadmaps with 511 concrete short-medium-to-long-term actions related to Forest management (FBA1), Mechanical wood processing (FBA2), Fibre-based biomaterial processing (FBA3), Biorefining (FBA4), and Bioenergy (FBA5). In addition, the report provides more detailed recommendations addressing a wide range of research, education, innovation and institutional needs related to the Top 3 Opportunity Pathways (OPs) of the five FBAs. The methodology of the project involved systematic critical issues analysis (drivers, barriers, threats and opportunities), a multi-stakeholder Delphi-like survey, global value network and business news analysis, visioning workshops and action roadmapping. This report is valuable for government, business, research and civil society actors interested in the state-of-the-art and the future of key forest-based bioeconomy areas in the world. Foresight researchers and practitioners will also find interesting methodological approaches, such as the consolidated action roadmaps. Finally, taking worldview perspective, while the project provided an opportunity for foresight knowledge transfer from Europe to Latin America, both the findings and methodology are equally relevant for other world regions and countries concerned with the future of the forest-based bioeconomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".