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Record W1883705188 · doi:10.1007/978-3-7908-1796-6_6

The Membership Problem for Probabilistic and Data Dependencies

2002· book-chapter· en· W1883705188 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

VenueStudies in fuzziness and soft computing · 2002
Typebook-chapter
Languageen
FieldComputer Science
TopicBayesian Modeling and Causal Inference
Canadian institutionsUniversity of OttawaUniversity of Regina
Fundersnot available
KeywordsDependency (UML)Bayesian networkConditional independenceProbabilistic logicDependency theory (database theory)Independence (probability theory)Relational databaseComputer scienceFunctional dependencyConditional probabilityTheoretical computer scienceData miningMathematicsArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

It has been suggested that Bayesian networks and relational databases are different because the membership problems for probabilistic conditional independence and embedded multivalued dependency do not always coincide. The present study indicates that the membership problems coincide on solvable classes of dependencies and differ on unsolvable classes. We therefore maintain that Bayesian networks and relational databases are the same in a practical sense, since only solvable classes of dependencies are useful in the design and implementation of both knowledge systems.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.202
GPT teacher head0.344
Teacher spread0.142 · 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