How to cope with changing demand conditions — The Swedish forest sector as a case study: an analysis of major drivers of change in the use of wood resources
Bibliographic record
Abstract
Promotion of renewable energy sources in Europe is foreseen to result in a dramatic increase in the demand for woody biomass. This paper assesses whether wood resources in the European Union (EU) will support future demand. Possible implications for countries with ample forest resources and a well-developed forest industry, such as Sweden, of an expected mounting demand pressure are discussed. Other drivers of change in global wood product markets posing challenges for the forest sector in general are also addressed. These drivers are reviewed and, together with the results from the EUwood project and econometric wood market models, analyzed as to their impacts on the Swedish forest sector. Demand is foreseen to vastly exceed the potential supply of woody biomass in Europe, putting a tremendous pressure on the Swedish forest resource and necessitating trade-offs between different ecosystem services. Further, projections suggest that Sweden will decrease in importance in production as well as consumption terms for all wood products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".