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Record W2133735693 · doi:10.5267/j.msl.2012.03.007

A new weighted fuzzy grammar on object oriented database queries

2012· article· en· W2133735693 on OpenAlexvenueno aff
Mohammad Pourbehzadi, Ali Haroonabadi, Mehdi Sadeghzadeh

Bibliographic record

VenueManagement Science Letters · 2012
Typearticle
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceObject (grammar)DatabaseGrammarFuzzy logicInformation retrievalProgramming languageData miningArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

The fuzzy object oriented database model is often used to handle the existing imprecise and complicated objects for many real-world applications. The main focus of this paper is on fuzzy queries and tries to analyze a complicated and complex query to get more meaningful and closer responses. The method permits the user to provide the possibility of allocating the weight to various parts of the query, which makes it easier to follow better goals and return the target objects.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.006
Open science0.0030.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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 teacher head, not a consensus.

Study designNot applicable
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".

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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