{"id":"W2023410739","doi":"10.1109/iscc.2010.5546594","title":"Component-based networking for simulations in medical education","year":2010,"lang":"en","type":"article","venue":"","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"U.S. National Library of Medicine","keywords":"Component (thermodynamics); Computer science; Graphics; Context (archaeology); Hierarchy; Set (abstract data type); Visualization; Computer graphics; Server; Wireless; Human–computer interaction; Multimedia; Distributed computing; Computer network; Operating system; Artificial intelligence; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001574058,0.0008218599,0.00055094,0.0005944652,0.0008599439,0.001967048,0.001442966,0.001481124,0.008212773],"category_scores_gemma":[0.004607096,0.0004746203,0.0004408308,0.0007415515,0.0008375176,0.001578908,0.001684834,0.001315645,0.001791691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083679,"about_ca_system_score_gemma":0.001044174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003025649,"about_ca_topic_score_gemma":0.003263503,"domain_scores_codex":[0.9988109,0.0007595628,0.00005084678,0.00006900491,0.0002696979,0.00003999945],"domain_scores_gemma":[0.9986438,0.0009443843,0.00003813127,0.0001692287,0.0001399482,0.00006447506],"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":[0.000152561,0.00009777991,0.0007849733,0.0003432432,0.00006494833,0.0001894629,0.0005180385,0.4114453,0.004163469,0.4309317,0.01488692,0.1364217],"study_design_scores_gemma":[0.00005571745,0.00004209233,0.0001146786,0.0000980435,0.00002297118,0.00007321302,0.00005425399,0.7831239,0.001816107,0.1251643,0.08941053,0.0000242849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003243998,0.0006845047,0.9777538,0.0008096038,0.0001550133,0.0001386443,0.00005272945,0.001895889,0.01526587],"genre_scores_gemma":[0.1616265,0.00196759,0.8237795,0.0002973806,0.0001240291,0.0008866629,0.0002810068,0.0006328821,0.01040443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008212773,"threshold_uncertainty_score":0.02747446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01307245739896758,"score_gpt":0.3081099652055343,"score_spread":0.2950375078065667,"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."}}