Effects of Organic Compost, Saturation, and Compaction on Air Permeability in Compost-Modified Soil for Landfill Biocover Systems
Bibliographic record
Abstract
Air permeability studies in landfill final cover systems (LFCS) remain limited, largely due to the complexity of specific tests related to gas flow in these systems. This research aimed to evaluate the effects of organic compost, saturation, and compaction on the intrinsic air permeability of organic compost–modified soils (OCMS). The potential benefits of these findings are substantial, particularly in improving landfill biocover systems and enhancing methane oxidation layers (MOL), which are critical for reducing methane emissions into the atmosphere. To achieve this, air permeability tests were conducted on three OCMS with soil-to-compost ratios of 3∶1, 1∶1, and 1∶3 (by weight) at optimum moisture content (OMC) to assess their hydro-geotechnical properties. Further tests were performed using a central composite design (CCD) to systematically vary key independent variables, including percentage of organic compost (POC), degree of saturation (DS), and degree of compaction (DC). These tests were complemented by additional assessments at OMC under maximum air permeability conditions. The results from the CCD model revealed that POC is the most influential variable affecting air permeability in OCMS, with higher POC leading to increased intrinsic air permeability (kai). The degree of saturation and compaction also had notable impacts, both contributing to a kai reduction. When DS and DC increased simultaneously, the decrease in kai became more pronounced. The intrinsic air permeability of the OCMS at OMC ranged from 5.01×10−15 to 3.47×10−14 m2. A few conditions met the criteria for the methane oxidation layer, emphasizing the importance of optimizing OCMS for practical applications in landfill biocover systems.
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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 | 0.001 |
| 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) | 0.001 | 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".