Bibliographic record
Abstract
The shift away from fossil fuels needed to reduce CO2 emissions requires the use of renewable carbon and energy sources, including biomass in the bioeconomy. Already today, the bioeconomy has a significant share in the EU economy with traditionally bio-based sectors. For the future, the energy, mobility and chemical sectors have additional high expectations of the bioeconomy, especially for agriculture and forestry to produce biomass as an industrial feedstock. Numerous studies have been published on the availability of feedstocks, but these often only look at individual applications. Looking at the total demand and considering the sustainability limits of biomass production leads to the conclusion that the expected demand for all industries that could process biomass exceeds the sustainably available capacity. To mitigate this conflict between feedstock demand and availability, it is proposed that the organic chemical sector be fully integrated into the bioeconomy and the energy sector be only partially integrated. In addition, recycling of wastes and residues including CO2 should lead to a circular bioeconomy. The purpose of this manuscript is to help fill the research gap of quantitatively assessing the demand and supply of biomass, to derive economic trends for the current transition phase, and to further develop the theoretical concept of the bioeconomy towards circularity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".