{"id":"W2060655775","doi":"10.1109/bigdata.2014.7004338","title":"Integrating existing large scale medical laboratory data into the semantic web framework","year":2014,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Metadata; Ontology; SNOMED CT; Relational database; Information retrieval; Data science; Semantic Web; Semantic grid; Semantic analytics; Semantic integration; Open Biomedical Ontologies; Analytics; Semantics (computer science); Social Semantic Web; Semantic Web Stack; World Wide Web; Terminology; OWL-S","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002445568,0.0001478312,0.0001973928,0.0000448214,0.0003725153,0.0002763513,0.004256344,0.0001395448,0.00006132862],"category_scores_gemma":[0.005228573,0.00008422107,0.00003130136,0.0004547884,0.0001319083,0.0004086613,0.002276612,0.0004486796,0.0001364205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001351639,"about_ca_system_score_gemma":0.0001637668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009455308,"about_ca_topic_score_gemma":0.001241131,"domain_scores_codex":[0.9980139,0.0002508684,0.0002919266,0.0005162663,0.0005642566,0.000362762],"domain_scores_gemma":[0.9961733,0.001505813,0.00008503423,0.002040502,0.00008250145,0.0001128258],"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":[0.0000022413,0.00008950628,0.01060254,0.00004928188,0.00002928875,0.00001755138,0.007398977,0.000004124719,0.0001796947,0.8733649,0.02032122,0.08794072],"study_design_scores_gemma":[0.0001678795,0.00003002468,0.000997621,0.0001595205,0.000008435171,0.00001025682,0.00221391,0.9250798,0.0002028291,0.01741812,0.05349525,0.000216373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.020286,0.0003392775,0.9592432,0.007509511,0.0007219701,0.00007717175,0.000001098278,0.0004132121,0.01140858],"genre_scores_gemma":[0.7749013,0.00002060916,0.2205044,0.004147777,0.0003147722,0.0000052327,0.000003531251,0.00000849255,0.0000938498],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9250757,"threshold_uncertainty_score":0.7909417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933545266083733,"score_gpt":0.3094103753499052,"score_spread":0.2800749226890679,"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."}}