Designing a Program to Reduce GHG Emissions and Generate Renewable Energy from Landfill Sites
Bibliographic record
Abstract
Emission reductions are achieved in the landfill sector through the capture and combustion of landfill gas (LFG). When organic matter in landfills decomposes in anaerobic conditions, methane is produced. According to the Intergovernmental Panel on Climate Change (IPCC), methane (CH4) is 21 times more harmful to the environment than CO2. To prevent this methane from escaping to the atmosphere, wells are drilled and a collection piping network is installed in the landfill. The LFG, which contains methane, is sucked into the network and carried to a combustion unit. The act of combustion destroys the methane and breaks it down into CO2, a much less potent GHG. The energy produced in the combustion process could also be used to make green electricity or steam for various purposes. In this article, the PERRL experience is used to describe how we implemented the program, what we have learned, and how you could use it to design similar programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".