Bibliographic record
Abstract
The 2030 Agenda for Sustainable Development and its Sustainable development Goals (SDGs) and the Paris Agreement on climate change provide a license to change the existing global economic model. Production and consumption needs to be transformed to be sustainable so that we can live within our planetary boundaries. An essential part of this transformation will be a forest-based circular bioeconomy, which builds on the world’s biggest land-based natural capital – forests – and the synergies of the circular economy and bioeconomy concepts. Biology, science, technology, resource efficiency and sustainability are laying the foundations for this transformation. Within this context, forest bio-based products have emerged that can substitute fossil-based materials like plastics, chemicals, synthetic textiles, cement and many other materials. This chapter analyses this ongoing transformation, focusing on forest-based products, and examines what this role looks like by exploring trends in current market structures and forest products in this transformation, and asks how will these new emerging bioeconomy markets develop? Central to this chapter are other questions such as what role do polices play in the transformation, and how do the forest companies and institutions evolve in the coming decades? It is important to acknowledge that there are many ecosystem services related to forest bioeconomy that play an even greater role in sustaining life on earth – and therefore facilitating the bioeconomy - than the products we address in this chapter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".