Field investigation and numerical modeling of MSW settlement in cold regions
Bibliographic record
Abstract
Understanding settlement of municipal solid waste (MSW) is crucial for landfill operators, especially during the filling stages, to optimize airspace capacity prior to landfill closure. In cold regions like Canada, MSW experiences partial freezing during winter months, which impacts biological processes. This chapter reports on the outcomes of a 12-year research project at the Sainte Sophie Landfill in Québec, Canada, focused on understanding MSW stabilization processes in cold environments. Settlement and temperature data were collected over the filling and post-closure phases, using 12 instrument bundles positioned at different depths across two vertical columns. The chapter focused on a landfill section, with the first three lifts deposited under freezing conditions and the remaining three lifts under warmer conditions. Analysis of temperature and settlement trends in lower lifts showed a lag of 12–18 months before temperatures reached a level supportive of biodegradation causing a delay in the biodegradation-induced settlement. Additionally, settlement data revealed resistant to compression in the lower lifts as MSW stiffness increased with the addition of upper layers. In addition to analyzing the field data, a thermal-mechanical-biological model, developed by the authors, was applied to compare the observed and simulated settlements and temperatures at the Sainte Sophie Landfill.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".