Recent advances in utilization of municipal solid waste for production of bioproducts: A bibliometric analysis
Bibliographic record
Abstract
Presently urban areas are reported for supporting 56% of the total world population, accountable for generating significant amount of municipal solid waste (MSW). Seventy percent of which ends into landfills, 19% is recycled and 11% is employed for energy generation. For environmental and economic sustainability , it is important to minimize the waste and maximize the recovery. This paper details the review from the Scopus and Web of Science databases, for the recent research in MSW utilization (conventional and novel). It identified the niche area of bioproducts production, to conduct the bibliographic review. The data confirms that major portion of the research is reported in North America. For production of bioproducts, the research on MSW in higher than research being pursued on organic fraction of municipal solid waste (OFMSW). Research studies on the valorization of MSW are aimed towards production of enzymes, surfactants and high value products , whereas on OFMSW, studies are aimed on the development of biomaterials and bioplastics . The most cited research articles are focused on the segregation of municipal waste and its valorization by microbial culture for production of high value products. MSW valorization at lab scale has given encouraging results but upscaling it to industrial level is challenging due the heterogeneity of the MSW.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.099 | 0.156 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".