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Record W4386129051 · doi:10.46254/in02.20220030

The Integrated Combined Compromise Solution Method and Distance-Based MCDM Model with Application

2022· article· en· W4386129051 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimization and Mathematical Programming
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMultiple-criteria decision analysisClosenessMathematical optimizationCompromiseComputer scienceVariance (accounting)Fuzzy logicMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This research compares the Combined Compromise Solution MCDM Method with some other known Multi-Criteria Decision-Making methods. We also propose an improved form with a new integration function based on a nonlinear optimization programming model with maximum kurtosis to the existing model. We also extend Ye and Li's fuzzy distance base model to include the centered possibilistic variance as one of the essential elements in calculating the relative closeness coefficient for alternatives in the decision-making process. The possibilistic model proposed by Ye and Li deviates in principle from the theory of possibility theory as formulated by Carlsson and Fuller. Towards the end of the paper, we discuss an approach to select the best Covid Vaccine within a pool of various other competitive, equally efficient vaccines.

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.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.168

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 science0.0000.000
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 head0.009
GPT teacher head0.220
Teacher spread0.212 · 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