The City of Calgary Biocell Landfill: Data Collection and Settlement Predictions Using a Multiphase Model
Bibliographic record
Abstract
The proper engineering of bioreactor landfills requires operation in a manner that maximizes waste decomposition and gas generation with the increased settlement. The landfill settlement occurs due to multiphase interactions of waste components such as solid, fluid and gas phases, with each phase exhibiting variations both in time and space. There are several mathematical models to evaluate the processes of biodegradation, gas generation, gas transport and distribution of moisture within a landfill. However, many existing waste settlement models focus on compression of waste solids but are unable to account for contribution from other phases. An effective model for landfill settlement was developed to consider settlement, gas generation and fluid transport simultaneously. A major obstacle to successful implementation of a multiphase settlement model is the amount of data required to calibrate and validate such a model. This manuscript describes research conducted to collect data from the City of Calgary Biocell Landfill (Calgary Biocell) and validate a multiphase settlement model. Results indicate that the prediction capability of settlement models can be improved by coupling the settlement mechanisms with the generation and dissipation of gas pressure and the moisture distribution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".