Mining for Recovery as an Option for Dumpsite Rehabilitation: A Case Study from Nagpur, India
Bibliographic record
Abstract
Conventionally, the concept of mining has been understood from the perspective of recovering metals from mineral ores and other valuable products from the earth. The mining concept is now being applied to old dumpsites and landfills for recovering materials out of municipal solid waste (MSW). Materials recovered from MSW can be turned into useful raw material for other purposes and allied industries. Plastics recovered from the dumpsites can be utilised as fuel in thermal power plants, cement and brick industries. Reclaimed earth could be utilised as fill or as a raw material in the construction industry. However, a detailed study is needed before mining an MSW dumpsite or a landfill, particularly to decide if the project would be economically sustainable. This paper describes a study conducted on a dumpsite situated in Nagpur, India, wherein the motivation was to rehabilitate the site after the removal of different constituents of MSW. Two scenarios were considered as the potential removal strategies: (a) mining for recovery and (b) transferring MSW from the dump to a new sanitary landfill. The study revealed that MSW mining for recovery is more economical and sustainable compared with putting MSW in a new sanitary landfill.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".