Geochemical Modeling Applied in Waste Disposal, and Its Relevance for Municipal Solid Waste Management
Bibliographic record
Abstract
Solid waste management is a challenge for municipalities mainly due to the increasing generation of residues worldwide. Much waste still ends up in landfills, either as a final disposal sink, or as a temporary yet long-term storage prior to eventual landfill mining. Through degradation and infiltration processes, these materials threaten groundwater, soil and the environment, invariably causing expressive societal concern. Waste managers, environmentalists and regulators alike focus upon predicting how these complex geochemical systems will evolve over decades. Geochemical modeling and transport simulation are commonly used to support the design, operation or characterization of these dynamic systems. In this targeted critical review of the literature over the last two decades concerning geochemical modeling applications to waste disposal (including municipal, incineration residue, and industrial), we have attempted to provide an assessment of the current state of knowledge in this field, supported by a comprehensive list of modeling methodologies and data analyses, relevant for municipal solid waste (MSW) management systems. The information provided is meant to be useful for specialists when planning, implementing or adapting MSW systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".