{"id":"W2068516765","doi":"10.5539/cis.v3n3p134","title":"Research on Patent Model Integration Based on Ontology","year":2010,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanghai Leading Academic Discipline Project; National Natural Science Foundation of China","keywords":"Computer science; Ontology; Semantics (computer science); Extensibility; Ontology-based data integration; Representation (politics); Software engineering; Openness to experience; Knowledge representation and reasoning; Description logic; Web Ontology Language; Information retrieval; Programming language; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001529411,0.00008342975,0.00008347946,0.0006207475,0.0003940972,0.0005723212,0.0008535775,0.00005340589,0.000003013671],"category_scores_gemma":[0.0001054343,0.00006043599,0.00001802118,0.0006424228,0.0003521334,0.003514191,0.0001975237,0.0002936741,0.00007860093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002650199,"about_ca_system_score_gemma":0.0001773272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001060454,"about_ca_topic_score_gemma":0.000005453678,"domain_scores_codex":[0.9986655,0.00003158191,0.0001946842,0.0002286283,0.0006086674,0.0002709747],"domain_scores_gemma":[0.9988861,0.0001609618,0.0000528007,0.0004559806,0.0003493734,0.00009477176],"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":[0.00001001713,0.00003180016,0.000115548,0.000004981217,5.531061e-7,6.739744e-7,0.001014385,0.009915362,0.00037825,0.7070684,0.0007302897,0.2807297],"study_design_scores_gemma":[0.0001686926,0.0002097245,0.01077009,0.00001131891,2.844221e-7,0.000004420287,0.00002760535,0.9839268,0.001686744,0.002326241,0.0007931491,0.00007496528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08501749,0.000001314366,0.8994299,0.001758061,0.000651185,0.0001161846,5.985829e-7,0.00008535812,0.01293986],"genre_scores_gemma":[0.9392639,0.000003058053,0.05816603,0.002519803,0.00002808887,0.00000832676,0.000001358219,0.000001012995,0.000008428196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9740114,"threshold_uncertainty_score":0.5518907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066630959673966,"score_gpt":0.3460021979561337,"score_spread":0.2393391019887371,"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."}}