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Record W2121931511 · doi:10.1109/tsmcc.2003.809360

A decision support system for interactive decision making-part II: analysis and output interpretation

2003· article· en· W2121931511 on OpenAlexaff
Liping Fang, Keith W. Hipel, D. Marc Kilgour, Xiaoyong Peng

Bibliographic record

VenueIEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsWilfrid Laurier UniversityToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsInterpretation (philosophy)Decision support systemManagement scienceOperations researchDecision analysisEngineeringComputer scienceMathematicsArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

For pt.I see ibid., p.42-55 (2003). The development of a comprehensive decision support system, GMCR II, for the systematic study of real-world interactive decision problems is presented. The companion paper (Part I), discusses how GMCR II elicits, stores, and manages conflict models; here (Part II), the focus is on GMCR IIs analysis and output interpretation subsystems. Specifically, this paper describes the powerful and efficient analysis engine contained in GMCR II, its informative output presentation and interpretation facilities, and a number of follow-up analyses. Furthermore, an illustrative case study is used to demonstrate how GMCR II can be conveniently applied in practice.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.038
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0380.014

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 head0.047
GPT teacher head0.355
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations222
Published2003
Admission routes1
Has abstractyes

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