A critical review of the transformation of biomass into commodity chemicals: Prominence of pretreatments
Bibliographic record
Abstract
Biomass production has been increasing steeply with rise in population across the globe. Majority of the produced biomass has been un-utilized and thrown to landfill or discarded without proper disposal. Due to production of greenhouse gasses, possible land, and groundwater contamination due to presence of trace organic contaminants (TrOCs), various countries across the globe have implemented stringent regulations or even banned landfill for waste deposition. On the other hand, depletion in available resources and ever-increasing product demand, utilization of waste to recover value added products has been need of the hour across the globe. Biomass, a low-cost and abundant nutrient resource having a tremendous potential for replacement for fossil fuels dependence. Pre-treatments like physical, chemical, and biological were applied on biomass for product specific application recovery. However, commercialization of recovered value-added products by utilizing biomass is still far from being achieved because of social unacceptability (i.e., public acceptance) due to the presence of contaminants. So, this review discusses about utilization of various pre-treatments for recovery of commodity chemicals from biomass and it addresses how the presence of TrOCs in biomass influences the recovery of products during their conversion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".