{"id":"W391464563","doi":"10.1007/978-3-319-06608-0_21","title":"Mining Contrast Subspaces","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Linear subspace; Contrast (vision); Computer science; Pruning; Set (abstract data type); Object (grammar); Time complexity; Data mining; Polynomial; Algorithm; Artificial intelligence; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007937732,0.0004253537,0.0004555455,0.0005027301,0.0003041316,0.0008195774,0.00391939,0.0002197928,0.0000173853],"category_scores_gemma":[0.00007400013,0.0003904744,0.00009006292,0.0004763553,0.0005905129,0.0003973831,0.001145793,0.0004856538,0.00009592775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001185617,"about_ca_system_score_gemma":0.0003121737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002064215,"about_ca_topic_score_gemma":0.00006440466,"domain_scores_codex":[0.9967936,0.0000196062,0.0004103895,0.001466734,0.0007134427,0.0005961893],"domain_scores_gemma":[0.9972503,0.0005126747,0.0002629253,0.00161724,0.0001687131,0.0001882101],"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":[6.841252e-7,0.000009689851,0.00002097596,0.00001125458,0.000005726312,0.00001660853,0.0003392757,0.001450171,0.00005064178,0.05168431,0.0001378645,0.9462728],"study_design_scores_gemma":[0.0002450391,0.0001270689,0.0001244486,0.0003447519,0.000008908682,0.00007655896,2.498675e-7,0.922883,0.0007091999,0.04902763,0.02564463,0.0008084659],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004881403,0.0002357589,0.9913848,0.001247261,0.0009762878,0.0002109709,0.000008777631,0.0002002682,0.005687066],"genre_scores_gemma":[0.04297439,0.00003416589,0.9536561,0.001536386,0.0006962584,0.00001771848,0.0000137336,0.00003607561,0.001035184],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9454643,"threshold_uncertainty_score":0.9998547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654142079109158,"score_gpt":0.2452489849794344,"score_spread":0.2287075641883428,"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."}}