BIO-SUCCINIC ACID: AN ENVIRONMENTFRIENDLY PLATFORM CHEMICAL
Bibliographic record
Abstract
Global research for biomass-based products is swelling gradually due to depletion of fossil-based raw material as well as their negative effects on the environment. The hazardous chemical processes used for production of valuable industrial chemicals are directly responsible for environmental pollution. Therefore, use of alternative renewable resources as feedstock to produce such chemicals is gaining popularity. Succinic acid (SA) is platform chemical which can be used to produce bulk industrial chemicals with huge global demand such as adipic acid, 1,4-butandiol, maleic anhydride. Non-hazardous biological fermentation process can be used to produce succinic acid, which can be further converted to these chemicals. The bio-based approach uses CO2 as supplement during the process and it replaces fossil-based raw materials; therefore, it is environment friendly. Mostly, pure sugars or sugars derived from crop residues or lignocellulosic materials are used to produce succinic acid. However, utilization of agricultural waste aromatic compounds to produce succinic acid has not been investigated in detail and not being implemented anywhere. Lignin waste management is a problem for the cellulosic bio-refineries and finding the way for its biological utilization is still in the stage of research and development. Unlike sugar metabolism, bioconversion of aromatic compounds is relatively complicated. Interestingly, bioconversion of agricultural waste aromatic compounds could be targeted towards production of succinic acid. The purpose of the present review is to highlight thispossibility.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".