{"id":"W2123581730","doi":"10.1109/ictai.2007.86","title":"Exploratory Quantitative Contrast Set Mining: A Discretization Approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Contrast (vision); Categorical variable; Discretization; Ranking (information retrieval); Set (abstract data type); Association rule learning; Computer science; Data mining; Measure (data warehouse); Interval (graph theory); Mathematics; Artificial intelligence; Machine 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":[],"consensus_categories":[],"category_scores_codex":[0.0004643864,0.00008634745,0.00008747281,0.00009456273,0.0001166787,0.0001262377,0.0004430033,0.00003250383,0.000008570862],"category_scores_gemma":[0.00003466112,0.00007386215,0.00002266609,0.0005268538,0.00004640742,0.0005510529,0.0001057914,0.00005247886,0.00005505029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001646624,"about_ca_system_score_gemma":0.00003132347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008513065,"about_ca_topic_score_gemma":0.000005845506,"domain_scores_codex":[0.9991457,0.00001978561,0.000175033,0.0003011182,0.0001609486,0.00019735],"domain_scores_gemma":[0.9993226,0.0001050612,0.00006065033,0.0003536576,0.00007093722,0.0000871108],"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":[0.000002709398,0.00008334651,0.0002280494,0.000004141511,0.00001105282,0.000002302505,0.003997423,0.00002045929,0.0003637631,0.9598187,0.004672326,0.03079578],"study_design_scores_gemma":[0.0006573483,0.0001638955,0.004458453,0.00001931057,0.00001170452,0.00001977527,0.01097811,0.9534018,0.002972558,0.002488192,0.0242744,0.0005544394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003850125,0.00002785608,0.9751623,0.0002435898,0.00006395018,0.0001134444,0.00001001515,0.0001873065,0.0203414],"genre_scores_gemma":[0.2327326,0.000003089265,0.7665185,0.0002192543,0.00003395285,0.00002446713,0.00005841813,0.00000657063,0.000403171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9573305,"threshold_uncertainty_score":0.3012011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05002360354283416,"score_gpt":0.3009474250412551,"score_spread":0.250923821498421,"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."}}