Briefing: Landfill mining for energy recovery in tropical developing countries
Bibliographic record
Abstract
This paper discusses landfill mining (LM) and uses data from tropical landfills in a preliminary analysis of its applicability. It is shown that there is a tendency of concentration of components with high calorific values over time, the contrary occurring with the municipal solid waste (MSW) water content, w, which is normally smaller for aged samples compared to that for fresh ones. This encourages LM adoption in various landfills since more energy can be recovered from MSW with the use of thermal recovery methods (TRMs) and less energy is necessary for MSW drying. Furthermore, it is demonstrated that 4–6 years is enough for most biological processes to occur in the field in tropical regions, making the use of LM possible in few years after landfill closing. The use of TRMs such as gasification is interesting because the produced hydrogen gas (H2) can be used for electrical power generation similar to methane (CH4). However, the use of thermal energy recovery methods in MSW components can result in dangerous atmospheric emissions, which must be controlled and rigorously monitored, as well as the stability of the MSW mass during the excavation process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".