An Extended Environmental Multimedia Modeling System (EEMMS) for Landfill Case Studies
Bibliographic record
Abstract
Traditional environmental multimedia models (EMMs) are usually based on one-dimensional (1D) and first-order assumptions, which may cause numerical errors in the simulation results. This study presents an extended EMM system (EEMMS) for landfill case studies, which incorporates numerical analysis. EEMMS includes four component modules: air, landfill, unsaturated zone, and saturated zone (groundwater) modules. The modules are solved within the EEMMS framework using both FEM (finite element) and FDM (finite difference) methods. The results obtained using EEMMS were evaluated by comparison with analytical solutions for pollutant multimedia transport under non-uniform and unsteady conditions. Modeling results showed that FEM and FDM produced better results compared to analytical outputs. Sensitivity analysis was also conducted for modeling of the retardation process. Experimental results from a pilot scale landfill site confirmed that the predicted emission flux was consistent with the measured flux in a spatial and temporal scheme. EEMMS may provide effective risk assessment through examining the fate and transport of pollutants in a multimedia environmental system and to help the subsequent management of the resulting environmental impacts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".