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Record W4328107717 · doi:10.1002/9781119569503.ch11

Selection of Wastewater Treatment for Small Canadian Communities

2023· other· en· W4328107717 on OpenAlexaffabout
Guangji Hu, Haroon R. Mian, Manjot Kaur, James Hager, Kasun Hewage, Rehan Sadiq

Bibliographic record

Venuenot available
Typeother
Languageen
FieldDecision Sciences
TopicMulti-Criteria Decision Making
Canadian institutionsUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsAnalytic hierarchy processWeightingVaguenessGrey relational analysisSelection (genetic algorithm)Fuzzy logicProcess (computing)Computer scienceOperations researchMathematicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

An integrated fuzzy analytic hierarchy process (F-AHP) and grey relational analysis (GRA) were used to facilitate the selection of appropriate wastewater treatment (WWT) alternatives for small communities. Seven commonly used WWT technology alternatives were assessed for a hypothetical small community in Canada. The assessment was based on the holistic evaluation of technical, economic, social, and environmental criteria, with each criterion composed of several subindices. The weights of criteria and subindices were determined using F-AHP to address nonprobabilistic uncertainties, such as vagueness and ambiguities in human thoughts resulting from the subjective weighting process. The weighted criteria were then aggregated and, based on the aggregation results, alternatives were ranked using GRA. The results from the integrated approach show that constructed wetland, stabilized pond, and extended aeration lagoon (EAL) are the top three appropriate WWT technologies for small Canadian communities. It was also found that the fuzzy-based approach and the nonfuzzy-based approach generated different rankings for the alternatives, indicating that fuzzy uncertainties could affect the decision-making process.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.477
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.365
GPT teacher head0.430
Teacher spread0.065 · 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
GenreOther

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

Citations0
Published2023
Admission routes2
Has abstractyes

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