{"id":"W2375735971","doi":"","title":"Semantic Search Strategy Of Heterogeneous Database Based On An Improved Particle Swarm Optimization","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Similarity (geometry); Particle swarm optimization; Ontology; Precision and recall; Semantic similarity; Data mining; Nearest neighbor search; Artificial intelligence; Information retrieval; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001074922,0.0006307524,0.001418678,0.001211545,0.0007512777,0.001370877,0.001526881,0.0009531988,0.001649431],"category_scores_gemma":[0.00186337,0.0004067528,0.0007537621,0.001105919,0.0005551087,0.001720185,0.000919141,0.0004415809,0.0001983903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001181187,"about_ca_system_score_gemma":0.001381915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008863778,"about_ca_topic_score_gemma":0.00479382,"domain_scores_codex":[0.9994272,0.0001460144,0.00004763743,0.0001132538,0.0001973233,0.00006860267],"domain_scores_gemma":[0.9996481,0.0001379313,0.00003250393,0.00003345509,0.0001135604,0.00003438388],"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":[0.0001386335,0.0001326778,0.001362356,0.00009464293,0.0001199025,0.0001175638,0.0001884038,0.8582032,0.003249844,0.0264682,0.002683357,0.1072412],"study_design_scores_gemma":[0.00002269983,0.00001894848,0.00009614429,0.000002393269,0.00001091893,0.00001643543,0.00001825554,0.9963152,0.0002503515,0.002989173,0.0002552941,0.000004196623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04505781,0.0002682062,0.9503973,0.0002545841,0.00005233986,0.00009107459,0.00004605191,0.0002408123,0.003591788],"genre_scores_gemma":[0.7307908,0.0002469422,0.2649832,0.0001707096,0.00004900143,0.0002834026,0.0001987982,0.00005353957,0.003223573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008863778,"threshold_uncertainty_score":0.01762438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04510727776690166,"score_gpt":0.2959685310629077,"score_spread":0.2508612532960061,"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."}}