Extended Environmental Multimedia Modeling System (EEMMS) with Analytic Hierarchy Process for Dual Evaluation of Energy Consumption and Pollutants in Solid Waste
Bibliographic record
Abstract
The dual assessment of environmental risks and energy consumption of solid waste is crucial for ensuring environmental safety and energy consumption management. Using risk assessment tools to inform best management practices for reclamation is very important. In this paper, a former Extended Environmental Multimedia Modeling System (EEMMS) combined with the Monte Carlo Method (MCM) of risk assessment was further used for exploring the fate and migration of pollutant leakage in the CFSWMA landfill. Specifically, MODFLOW combined with the EEMMS-MCM system has been applied using Biochemical Oxygen Demand (BOD) as a typical indicator to model the behavior of leachate components. An EEMMS-MCM integrated risk assessment for a 20-year period was conducted. The case study of BOD emissions from the CFSWMA landfill shows that even the leachate did not have a serious impact on Canadian territory during the 20 years; however, non-sorption chemicals are mainly affected by the groundwater flow, whereas sorption chemicals are affected by the partition coefficient (or sorption). Further, this study introduces energy consumption factors such as soil and surface water bodies, and constructs an integrated dual assessment framework for the environmental risks and energy consumption of pollutants. In summary, by integrating the EEMMS pollutant migration model with an environmental risk and energy consumption assessment, a dual assessment of environmental risks and energy consumption is achieved.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".