{"id":"W2130215687","doi":"10.5539/cis.v7n2p36","title":"3D Virtual World Retrieval Based on Ontology and Content","year":2014,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; VRML; Metaverse; Ontology; Information retrieval; Precision and recall; SPARQL; Construct (python library); Metric (unit); Semantics (computer science); Similarity (geometry); Virtual reality; World Wide Web; Semantic Web; Artificial intelligence; Image (mathematics); RDF","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002611384,0.00004742789,0.00006659206,0.0002080327,0.00008262723,0.0001032543,0.00006519732,0.00001315938,0.000003630252],"category_scores_gemma":[0.00001714264,0.00003917807,0.00000910063,0.0002044284,0.00007734849,0.0006979823,0.00001875784,0.00004536637,0.00001095755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001048845,"about_ca_system_score_gemma":0.000007613282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001651024,"about_ca_topic_score_gemma":8.86313e-7,"domain_scores_codex":[0.9996119,0.00000529094,0.0001139683,0.00005901494,0.0001197793,0.00009004679],"domain_scores_gemma":[0.9997825,0.000031146,0.00001513358,0.0000732881,0.00004373286,0.00005416776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001186514,0.000006278219,0.0008459662,0.00002499941,0.000005004371,1.709719e-7,0.0007075696,0.5673693,0.0001649979,0.004939097,0.0003618235,0.425563],"study_design_scores_gemma":[0.0001461489,0.00004189454,0.003103499,0.000009548958,0.000001822851,9.228015e-7,0.000008376124,0.9948376,0.0001215403,0.00001017307,0.001666834,0.00005163542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1621688,0.000006780907,0.834071,0.00008932293,0.000143918,0.00002144928,7.357755e-7,0.00005364989,0.003444366],"genre_scores_gemma":[0.9967974,0.000005315671,0.002326443,0.0008339268,0.00002320291,4.233099e-7,0.000001707963,0.00000106757,0.00001055017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8346286,"threshold_uncertainty_score":0.1597635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01489860193788187,"score_gpt":0.2062484933678555,"score_spread":0.1913498914299736,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}