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Artificial learning approaches for the nextgeneration web: Part II

2008· article· es· W4244486989 on OpenAlexaff
Jorge Rafael Gutiérrez-Pulido, María Andrade-Aréchiga, José Román Herrera Morales, María Eugenia Cabello-Espinosa, Steve Legrand, Dave Elliman

Bibliographic record

VenueIngeniería Investigación y Tecnología · 2008
Typearticle
Languagees
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsCentrale des Syndicats du Québec
FundersDivision of Mathematical SciencesUniversity of NottinghamInstituto Tecnológico y de Estudios Superiores de MonterreyCentro de Investigación Científica y de Educación Superior de Ensenada, Baja CaliforniaGriffith UniversityUniversidad de Colima
KeywordsOntologySemantic WebComputer scienceRelation (database)Point (geometry)Ontology learningOWL-SProcess (computing)Domain (mathematical analysis)Semantic Web StackSocial Semantic WebInformation retrievalOntology Inference LayerUpper ontologyArtificial intelligenceWorld Wide WebData miningSuggested Upper Merged OntologyMathematicsProgramming language

Abstract

fetched live from OpenAlex

Ingeniería Investigación y Tecnología es una publicación trimestral, arbitrada, de investigación científica y tecnológica editada e impresa por la Facultad de Ingeniería de la UNAM. Su propósito es difundir información y conocimientos originales e inéditos relacionados con la ingeniería. Es un espacio para profesores, investigadores, profesionales y estudiantes de todo el mundo.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.160
GPT teacher head0.265
Teacher spread0.105 · 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 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

Citations3
Published2008
Admission routes1
Has abstractyes

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