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Record W2137243467 · doi:10.1177/0734242x13507310

A framework for a decision support system for municipal solid waste landfill design

2013· article· en· W2137243467 on OpenAlexafffund
Ashley Verge, R. Kerry Rowe

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeomembraneLeachateMunicipal solid wasteComponent (thermodynamics)Decision support systemEngineeringWaste managementService lifeService (business)Waste collectionCivil engineeringComputer scienceBusiness

Abstract

fetched live from OpenAlex

A decision support system (Landfill Advisor or LFAdvisor) was developed to integrate current knowledge of barrier systems into a computer application to assist in landfill design. The program was developed in Visual Basic and includes an integrated database to store information. LFAdvisor presents the choices available for each liner component (e.g. leachate collection system, geomembrane liner, clay liners) and provides advice on their suitability for different situations related to municipal solid waste landfills (e.g. final cover, base liner, lagoon liner). Unique to LFAdvisor, the service life of each engineered component is estimated based on results from the latest research. LFAdvisor considers the interactions between liner components, operating conditions, and the existing site environment. LFAdvisor can be used in the initial stage of design to give designers a good idea of what liner components will likely be required, while alerting them to issues that are likely to arise. A systems approach is taken to landfill design with the ultimate goal of maximising long-term performance and service life.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.048
GPT teacher head0.331
Teacher spread0.283 · 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.

Study designNot applicable
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

Citations8
Published2013
Admission routes2
Has abstractyes

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