{"id":"W2368126280","doi":"","title":"Semantic Modeling Method of IDEF1x and It's Application in Database Designing","year":2005,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Database design; Database; Information retrieval; Data mining; Semantic data model; Database model; IDEF1X; Semantic Web","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":[0.004422844,0.0007796074,0.0006531946,0.00208302,0.0009674818,0.003063957,0.002007723,0.001092215,0.004339441],"category_scores_gemma":[0.004004737,0.0004880185,0.00194499,0.001433688,0.001224111,0.006942224,0.001523272,0.001725654,0.0009942893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343568,"about_ca_system_score_gemma":0.002289382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003331353,"about_ca_topic_score_gemma":0.001445453,"domain_scores_codex":[0.996941,0.0008978083,0.00036947,0.0005895615,0.001029115,0.0001730106],"domain_scores_gemma":[0.9988532,0.0003893232,0.00007142239,0.0002992185,0.0003316931,0.00005504597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009189785,0.00006812057,0.001269407,0.0003237604,0.00006096326,0.0003499115,0.001311258,0.008813288,0.003574438,0.7586786,0.01072028,0.2147382],"study_design_scores_gemma":[0.0001225402,0.0001519655,0.0009148894,0.0004364993,0.0001720136,0.002465116,0.0005576829,0.1686195,0.01939282,0.3364269,0.4705834,0.0001566321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001677186,0.0003045751,0.9902642,0.000276361,0.00007299423,0.00006873962,0.0001076594,0.0007790158,0.006449209],"genre_scores_gemma":[0.08498524,0.001515217,0.8993028,0.0003641228,0.0001600464,0.0004783276,0.0009613652,0.000567334,0.01166542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004422844,"threshold_uncertainty_score":0.02339053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101064484208551,"score_gpt":0.3230904716907234,"score_spread":0.3020798268486378,"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."}}