Performance of Biocover in Mitigating Fugitive Methane Emissions from Municipal Solid Waste Landfills in Cold Climates
Bibliographic record
Abstract
Manipulation of landfill covers to maximize oxidation capacity provides a promising complementary strategy for the control of methane emissions from landfills. Engineered biocovers (i.e., a mixture of soil and organic matter) can be used for gas emission control in landfills. One of the key factors affecting methane oxidation and methane removal by engineered biocovers in general is the temperature in the biocover material, which is in turn influenced by the ambient temperature. This study compares the efficacy of methane removal in a biocover consisting of a mature compost and soil mixture at different temperatures using three column tests. Methane removal efficiencies and temporal variation of methane mass flux were determined based on measured methane content at different locations within the biocover material profiles. In all cases, methane content at the top was considerably lower than that of the inflow. However, the efficiency of the column at 22°C was significantly higher than that of the one placed at 11°C. Based on scanning electron microscopy images taken from the upper section of the columns, evident formation of stacks of materials and reduction of pore spaces in all cases were observed and were interpreted qualitatively as a consequence of biological activity leading to formation of bacterial colonies, biofilm, or carbonate precipitate over biocover material particles.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".