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Record W4244377413 · doi:10.4018/9781591405603.ch081

Proper Placement of Derived Classes in the Class Hierarchy

2011· book-chapter· en· W4244377413 on OpenAlexaff
Reda Alhajj, Faruk Polat

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsClass (philosophy)Class hierarchyComputer scienceBase (topology)HomogeneousSelection (genetic algorithm)Object (grammar)HierarchyTheoretical computer scienceArtificial intelligenceMathematicsObject-oriented programmingProgramming languageCombinatorics

Abstract

fetched live from OpenAlex

Users may derive new classes by defining views1 based on the current database contents. Some virtual classes are classified as brothers of existing classes, and others are either superclasses or subclasses of existing base and virtual classes. A base class is defined directly by the user using class definition constructs. A virtual class is classified as a brother of another class if it is derived from the latter class via a selection. To have a homogeneous system, virtual classes must be treated as first-class citizens in an object-oriented model. Request access from your librarian to read this chapter's full text.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0230.015

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 head0.030
GPT teacher head0.226
Teacher spread0.196 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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