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Record W2377280609

Database Model Based on Spatio-temporal Ontology

2010· article· en· W2377280609 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

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0010.000
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 head0.010
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