{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005800865,0.0007530985,0.001103176,0.0006581949,0.0004385764,0.0006156115,0.003910136,0.001165805,0.00004161304],"category_scores_gemma":[0.0007547014,0.0006214489,0.0003281094,0.00142095,0.0003545902,0.0004316069,0.000507488,0.001577497,0.0004139436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003070614,"about_ca_system_score_gemma":0.001006514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001007227,"about_ca_topic_score_gemma":0.00002086787,"domain_scores_codex":[0.9911281,0.0001228826,0.001525246,0.001601445,0.003938182,0.001684184],"domain_scores_gemma":[0.9942455,0.002211483,0.0004731368,0.000743848,0.0004691724,0.001856806],"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":[0.0007661193,0.01097851,0.02790703,0.01343385,0.001250948,0.008464761,0.01025011,0.001486515,0.01561799,0.6276427,0.08732472,0.1948768],"study_design_scores_gemma":[0.001632763,0.0008892373,0.000294666,0.004471185,0.00003514113,0.0006536305,0.00006483636,0.9794854,0.002836212,0.0007049197,0.007999632,0.0009323629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05683148,0.0004660496,0.9178815,0.007516285,0.01148266,0.0007373269,0.000001279915,0.00168806,0.003395291],"genre_scores_gemma":[0.9778199,0.00003472729,0.01477636,0.002901219,0.004255145,0.00007329243,0.000002695951,0.00006617892,0.00007048825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9779989,"threshold_uncertainty_score":0.9996237,"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."}}