An overview of factors affecting the rate of generation and Physical Composition of Municipal Solid Waste
Bibliographic record
Abstract
Abstract An efficient waste management strategy requires a detailed waste characterization, quantification and prediction of the rate of generation and physical components of waste, which is also an index of the recoverable energy. A comprehensive data of waste generation and composition creates awareness for the concerned policy makers and organization responsible for waste management. The objective of studies on estimation of waste generation and physical content is to provide baseline data for effective waste management planning. Several factors account for the variation in the rate of generation and the physical content of waste generated at different places. This study aims at identifying and analyzing some of these factors. The review reveals that waste generation and composition follows different patterns in different places at different locations at different times and season. A major factor that influences the rate of generated waste per year and the percentage composition of each physical waste stream is the socio-economic level. Moreover, the organic stream of the waste depends largely on the income level. High content of organic waste is identified in the low-income region, this paper, therefore, recommends the setting up of treatment facilities for organic waste at the organic-rich region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".