Chemicals and energy from biomass
Bibliographic record
Abstract
Approximately 89 million metric tonnes of organic chemicals and lubricants are produced annually in the United States (T.M. Carole, J. Pellegrino, and M.D. Paster. Appl. Biochem. Biotechnol. 115, 871 (2004)). The majority of these materials are fossil fuel based and may load the environment during use and at the end of their life cycle. Issues, such as disposal, pollution, and degradation, must be considered and weighed. As a result, the need to decrease pollution caused by petrochemical usage is currently impelling the development of green technologies. It is virtually inarguable that the dwindling hydrocarbon economy will eventually become unsustainable. The cost of crude oil continues to increase, while agricultural products see dramatic decreases in world market prices. These trends provide sufficient basis for renewed interest in the use of biomass as a feedstock and for the development of a carbohydrate-based economy as the logical alternative to fossil fuel resources.Key words: biomass, biochemicals, natural products, bioenergy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".