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Record W4299537821 · doi:10.1002/9780470050118.ecse920

Computational Intelligence

2008· other· en· W4299537821 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

VenueWiley Encyclopedia of Computer Science and Engineering · 2008
Typeother
Languageen
FieldComputer Science
TopicRough Sets and Fuzzy Logic
Canadian institutionsUniversity of AlbertaUniversity of Manitoba
Fundersnot available
KeywordsComputational intelligenceRough setGenetic programmingComputer scienceArtificial neural networkArtificial intelligenceContext (archaeology)Soft computingFuzzy logicIntelligent decision support systemSet (abstract data type)Fuzzy setGenetic algorithmNeuro-fuzzyMachine learningFuzzy control system

Abstract

fetched live from OpenAlex

Abstract Several interpretations of the notion of computational intelligence (CI) exist. Computationally intelligent systems have been characterized by Bezdek relative to adaptivity, fault‐tolerance, speed, and error rates. In its original conception, many technologies belonged to computational intelligence, namely, neural networks, genetic algorithms, fuzzy systems, evolutionary programming, and artificial life. More recently, rough set theory and its extensions to approximate reasoning and real‐time decision systems have been considered in the context of computationally intelligent systems. Overall, CI can be regarded as a synergy of genetic, fuzzy, rough, and neural computing.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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