Design of Microbial Methane Oxidation Systems for Landfills
Bibliographic record
Abstract
Landfill methane currently represents the largest global source of greenhouse gas emissions from the solid waste sector. Emissions are expected to increase due to increasing waste generation, particularly in countries still landfilling biodegradable wastes. As a complementary measure to gas extraction with subsequent flaring or energy conversion, or for emissions reduction from old landfills or from landfills containing wastes with a low gas potential, microbial methane oxidation systems (MMOS) are considered a promising technology. Numerous studies relating to controlling factors and enhancement of microbial methane oxidation in biocovers, biowindows or biofilters, both in laboratory and in large scale field settings, have been published. The design of optimized MMOS requires thorough understanding of the involved processes, specifically the biological ones and of those related to the transport of gas and water in porous media, and of the impact of material properties and external environmental factors on these processes. Consequently, the selection of materials that are suitable from a biogeochemical and from a geotechnical point of view, meeting the required water and gas transport properties, are key aspects in the design process. This paper reviews the scientific background of the relevant concepts and processes dictating MMOS performance, and provides guidance on layout and design steps, including choice of materials and quality control. Further, a decision tree to support the choice of MMOS is proposed. This paper provides the scientific foundation for upcoming technical guidance documents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".