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Record W2377280609

Database Model Based on Spatio-temporal Ontology

2010· article· en· W2377280609 on OpenAlexaff
Jianjun Zhu

Bibliographic record

VenueGeography and Geo-Information Science · 2010
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOntologyComputer scienceOntology-based data integrationSuggested Upper Merged OntologySemantics (computer science)Process ontologyInformation retrievalUpper ontologyOntology Inference LayerTupleDatabaseOWL-SSemantic WebProgramming languageSemantic Web StackMathematics
DOInot available

Abstract

fetched live from OpenAlex

The primary purpose of building the spatio-temporal(S-T) ontology is to explicitly represent S-T information contained the commonly-perceived knowledge,and to realize the sharing of information between different disciplines.From the perspective of ontology,this paper conceptualizes the dynamic changes for the geographical phenomena and things,and further refines the S-T ontology which is classified as the S-T object ontology,S-T event ontology and S-T process ontology.From the perspective of database modeling,this paper conducts these types of S-T ontology on semantics-enhanced descriptions,conceptual model schematizations,tuple expression,and semantic-based query.This proposed model can help improving on descriptions of dynamic changes for geographical phenomena and things.In addition,the inherent relationships between three ontologies can demonstrate the causal of changes.Finally,the model has been applied into sea-ice phenomena varying with time,and then verified its feasibility.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0060.010
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.010
GPT teacher head0.273
Teacher spread0.263 · 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 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

Citations3
Published2010
Admission routes1
Has abstractyes

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