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Record W3089087403 · doi:10.3390/min10100846

Geochemical Modeling Applied in Waste Disposal, and Its Relevance for Municipal Solid Waste Management

2020· article· en· W3089087403 on OpenAlexaff
Francisco S. M. Araujo, Hugo Fantucci, Everton Barbosa Nunes, Rafael M. Santos

Bibliographic record

VenueMinerals · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMunicipal solid wasteIncinerationWaste managementEnvironmental scienceWaste disposalEnvironmental planningEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.254
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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