The bioeconomy and the forestry sector: Changing markets and new opportunities
Bibliographic record
Abstract
The bioeconomy is expected to replace the current information economy and will depend heavily on the manufacturing and trade of bioproducts, which are all commodities generated by living organisms through the use of technology and biotechnology, as well as non-timber forest products. In Canada alone, markets for bioproducts are in excess of $100 billion annually. The bioeconomy should impact most of Canada's economic sectors: energy and transportation, food and agro-food, pharmaceuticals, nutraceuticals, forestry, materials and manufacturing, waste management and a large variety of consumer goods. The bioeconomy holds promises to wean the Canadian economy from its dependence on fossil fuels as a primary source of energy as well as platform chemicals in materials and manufacturing, while meeting the Kyoto commitments on greenhouse gas reductions. Finally, the bioeconomy will reduce the environmental impact of economic growth by increasing the use of industrial and urban wastes and developing goods that are biodegradable. Key words: NTFP, ecology, forest economics, biomass, energy, pharmaceuticals, forest policies, agroforestry
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".