Deformation of MSW Bioreactor Landfills: Properties and Analysis Approach
Bibliographic record
Abstract
Bioreactor landfills are operated for rapid stabilization of waste, increased landfill gas generation for cost-effective energy recovery, increase in landfill space, enhanced leachate treatment, and reduced post closure maintenance period. Due to rapid stabilization and settlement of solid waste, bioreactor landfills are gaining popularity as an alternative to conventional Subtitle D landfills. However, the addition of leachate to accelerate waste decomposition changes the physical and engineering characteristics of Municipal Solid Waste (MSW), which affects the compressibility and shear strength behavior of MSW. Settlement during the active landfilling period is beneficial as it increases the landfill capacity, however, large differential settlement may cause serious damage to the existing leachate recirculation pipe system and interim covers. Also, due to accelerated decomposition and changes in shear strength properties, the stability of landfill slopes is expected to be affected. The objective of this paper is to analyze the compressibility of MSW in a bioreactor landfill as a function of construction sequence, time and waste placement using the finite element program PLAXIS. In this analysis, the layer properties are adjusted to account for extent of decomposition. The results from PLAXIS are compared with waste settlement data collected during the filling of a landfill cell at Calgary Biocell in Canada.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".