The reduction of greenhouse gas emissions using various thermal systems in a landfill site
Bibliographic record
Abstract
In this paper, the Greenhouse Gas (GHG) emissions from an uncontrolled landfill site filled with Municipal Solid Waste (MSW) are compared with those from controlled sites in which collected Landfill Gases (LFG) are utilised by various technologies. These technologies include flaring, conventional electricity generation technologies such as Internal Combustion Engine (ICE) and Gas Turbine (GT) and an emerging technology, Solid Oxide Fuel Cell (SOFC). The results show that SOFC is the best option for reducing the GHG emissions among the studied technologies. In the case when SOFC is used, GHG emissions from the controlled site are reduced by 63% compared to the uncontrolled site. This case has a specific lifetime GHG emission of 2.38 tonnes CO2 .eq/MWh when only electricity is produced and 1.12 tonnes CO2.eq/MWh for a cogeneration application.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".