Product developments in the bio‐based chemicals arena
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
Abstract Around the world, significant able steps are being taken to move from today's fossil‐based economy to a more sustainable economy based on biomass. A key factor in the realization of a successful bio‐based economy will be the development of biorefinery systems allowing highly efficient and cost‐effective processing of biological feedstocks to a range of bio‐based products, and successful integration into existing infrastructure. The recent climb in oil prices and consumer demand for environmentally friendly products has now opened new windows of opportunity for bio‐based chemicals and polymers. Industry is increasingly viewing chemical and polymer production from renewable resources as an attractive area for investment. Within the bio‐based economy and the operation of a biorefinery, there are significant opportunities for the development of bio‐based building blocks (chemicals and polymers) and materials (fiber products, starch derivatives, etc.). In many cases this happens in conjunction with the production of bioenergy or biofuels. The production of bio‐based products could generate US $10–15 billion ofrevenue for the global chemical industry. The economic production of biofuels is often a challenge. The co‐‐production of chemicals, materials food and feed can generate the necessary added value. This paper highlights all bio‐based chemicals with immediate potential as biorefinery ‘value added products’. The selected products are either demonstrating strong market growth or have significant industry investment in development and demonstration programs. The full IEA Bioenergy Task 42 report is available from http://www.iea-bioenergy.task42-biorefineries.com © Her Majesty the Queen in Right of Canada 2012
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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