Determination of Waste Properties from Settlement Behaviour of a Full Scale Waste Cell Operated As a Landfill Bioreactor
Bibliographic record
Abstract
The proper engineering of landfill bioreactors require operation in a manner that maximizes waste decomposition and gas generation while making use of the settlement that occurs. Settlement in landfills consists of interacting multiphase media with each phase exhibiting variations both in time and space. There are mathematical models available to evaluate the processes of biodegradation, gas generation and transport and distribution of moisture within a landfill. At the same time, many existing waste settlement models focus on compression of waste solids but overlook the contribution from other phases. An effective model for landfill settlement should be able to consider settlement, gas generation and fluid transport simultaneously. However, the phase interactions involved in landfill settlement are not well understood. Apart from the theoretical uncertainties in the phase interactions, a major obstacle to developing a multiphase settlement model is the amount of data required to calibrate and validate the model. This manuscript presents data from study of a full scale waste cell currently operated as a landfill bioreactor. The waste cell is equipped with gas collection and leachate recirculation systems, and is instrumented to collect time resolved data. This waste cell is being used as a research facility to study the impact of leachate recirculation on gas production and settlement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".