{"id":"W2159937564","doi":"10.1109/cbms.2007.70","title":"Medical Knowledge Morphing via a Semantic Web Framework","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE Symposium on Computer-Based Medical Systems","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Morphing; Computer science; Domain knowledge; Knowledge base; Ontology; Leverage (statistics); Semantic Web; Modalities; Knowledge management; Open Knowledge Base Connectivity; Information retrieval; Artificial intelligence; Personal knowledge management; Organizational learning","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":[0.005651443,0.0006873833,0.0008019804,0.00457195,0.0017768,0.007417288,0.002021946,0.002065449,0.002648605],"category_scores_gemma":[0.00507293,0.0006578091,0.002336019,0.003485447,0.004230778,0.009537889,0.004787009,0.002334321,0.0008570705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001561747,"about_ca_system_score_gemma":0.003060596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004431026,"about_ca_topic_score_gemma":0.004097087,"domain_scores_codex":[0.9963444,0.001415292,0.0004667966,0.0005199857,0.001055869,0.0001976952],"domain_scores_gemma":[0.9972051,0.001310853,0.0002004815,0.0007180046,0.0003664419,0.0001991678],"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":[0.00006091784,0.00008647443,0.0003784035,0.0002175937,0.00008228816,0.0007391068,0.0008034509,0.01486968,0.002567199,0.8833387,0.003902713,0.09295345],"study_design_scores_gemma":[0.00003404675,0.00003137659,0.0002525024,0.0001986794,0.00009624907,0.0005474617,0.0003982806,0.1008421,0.004543069,0.8024912,0.09051196,0.00005298797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002880114,0.0005647382,0.9854939,0.001684737,0.00007749745,0.0001336235,0.0001756616,0.001196353,0.007793325],"genre_scores_gemma":[0.0903891,0.001051409,0.903955,0.0005537416,0.0001261042,0.0002181351,0.0006871937,0.0001590214,0.002860177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007417288,"threshold_uncertainty_score":0.02988803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01675939939361247,"score_gpt":0.2744476933310398,"score_spread":0.2576882939374273,"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."}}