{"id":"W2096692332","doi":"10.1007/978-3-642-23199-5_11","title":"GENCCS: A Correlated Group Difference Approach to Contrast Set Mining","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Contrast (vision); Computer science; Set (abstract data type); Data mining; Group (periodic table); Property (philosophy); Task (project management); Artificial intelligence; Space (punctuation); Value (mathematics); 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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006710158,0.0006183417,0.0005985755,0.0008357196,0.0003495385,0.0006704769,0.005387432,0.0003238377,0.00001916043],"category_scores_gemma":[0.00007122941,0.0005629013,0.0001070148,0.001001774,0.0004997169,0.0003562965,0.002016325,0.0007452026,0.0001288612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001777998,"about_ca_system_score_gemma":0.0002887678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006551354,"about_ca_topic_score_gemma":0.00002662802,"domain_scores_codex":[0.9955633,0.00003099186,0.000586818,0.002205163,0.0007812483,0.0008325282],"domain_scores_gemma":[0.9968635,0.0002958861,0.000274985,0.001979355,0.0002028635,0.0003833842],"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":[0.000004697824,0.00008156611,0.00004044018,0.0000226406,0.00001766355,0.00002716145,0.003297177,0.001388028,0.00008563613,0.06171502,0.0001786982,0.9331413],"study_design_scores_gemma":[0.0003467142,0.0002855339,0.0007084993,0.0004483853,0.00001924734,0.000157414,0.000001153279,0.9546876,0.0001562846,0.03846909,0.003415831,0.001304232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001680473,0.0001664537,0.986394,0.0001802487,0.0008859893,0.0005764468,0.00004025563,0.0002617034,0.0113268],"genre_scores_gemma":[0.05089965,0.0000227857,0.945926,0.001737587,0.0002725282,0.00006838146,0.00004360279,0.00004632382,0.0009831215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9532996,"threshold_uncertainty_score":0.9999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331577624955423,"score_gpt":0.2398708847056504,"score_spread":0.2067131222101081,"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."}}