Diversification of the forest industries: role of new wood-based products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study identifies new wood-based products with considerable potential and attractive markets, including textiles, liquid biofuels, platform chemicals, plastics, and packaging. We apply a mixed-methods review to examine how the position of the forest industry in a given value chain determines the respective production value. An assessment is provided as to the degree to which these emerging wood-based products could compensate for the foreseen decline of graphic paper markets in four major forest industry countries: USA, Canada, Sweden, and Finland. A 1%–2% market share in selected global markets implies a potential increase in revenues of 18–75 billion euros per annum in the four selected countries by 2030. This corresponds to 10%–43% of the production value of forest industries in 2016 and compares with a projected decline of graphic paper industry revenue of 5.5 billion euros by 2030. The respective impacts on wood use are manifold, as many of the new products utilize by-products as feedstock. The increase in primary wood use, which is almost entirely attributed to construction and to some extent textiles markets, would be in the range of 15–133 million m 3 , corresponding to 2%–21% of the current industrial roundwood use in the selected countries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it