{"id":"W2743348008","doi":"","title":"Utilizing semantics in itemset mining and jointree probability propagation","year":2006,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Bayesian network; Computer science; Soundness; Data mining; Semantics (computer science); Node (physics); Conditional probability; Inference; Probabilistic logic; Artificial intelligence; Theoretical computer science; Machine learning; Mathematics; 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":[0.005847928,0.001034497,0.001171035,0.004556944,0.001104219,0.002849731,0.002261259,0.001192375,0.00180226],"category_scores_gemma":[0.0296022,0.0008722845,0.002023122,0.005066251,0.00240318,0.01071495,0.002538365,0.001398196,0.000600561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302458,"about_ca_system_score_gemma":0.0019742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003485024,"about_ca_topic_score_gemma":0.004653581,"domain_scores_codex":[0.9957789,0.001668103,0.0003249839,0.0007619927,0.001273455,0.0001924983],"domain_scores_gemma":[0.9847569,0.0102083,0.0009941054,0.002161924,0.001660923,0.000217921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002751,0.0002362531,0.009749766,0.0004485147,0.000244271,0.0003212563,0.0008933877,0.2256884,0.005284127,0.350701,0.00149517,0.4046627],"study_design_scores_gemma":[0.00002829897,0.00008696253,0.000856023,0.00005598955,0.00009774674,0.0002322044,0.0001116501,0.6446879,0.004153085,0.347191,0.002457253,0.00004198841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007313902,0.00008573796,0.9916986,0.00009160452,0.000007158603,0.00005228914,0.00004746591,0.0001639295,0.0005392687],"genre_scores_gemma":[0.2633982,0.0003446155,0.7343379,0.0001161041,0.00004804895,0.0002449051,0.0003070665,0.00009288018,0.001110135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005847928,"threshold_uncertainty_score":0.03092718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505948535647364,"score_gpt":0.2179739704329141,"score_spread":0.2029144850764405,"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."}}