{"id":"W1993246951","doi":"10.1016/j.ins.2003.03.015","title":"CLIP4: Hybrid inductive machine learning algorithm that generates inequality rules","year":2003,"lang":"en","type":"article","venue":"Information Sciences","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Pruning; Missing data; Algorithm; Feature (linguistics); Inductive bias; Tree (set theory); Data mining; Benchmarking; Machine learning; Artificial intelligence; Mathematics; Multi-task 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":[0.001386247,0.0001187113,0.0001152959,0.0001694776,0.0007081832,0.0008703739,0.0007776666,0.00002882352,0.0000278478],"category_scores_gemma":[0.0001748894,0.00009529002,0.00003349412,0.0006217095,0.0001649161,0.005639135,0.0001453046,0.0001457979,0.0002423353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002702898,"about_ca_system_score_gemma":0.0001192069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001484567,"about_ca_topic_score_gemma":0.000003043731,"domain_scores_codex":[0.998668,0.00009283007,0.0003035696,0.0002265819,0.000457655,0.0002514026],"domain_scores_gemma":[0.9992023,0.00009959064,0.0002302992,0.0002650615,0.0001218039,0.00008099181],"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":[4.447427e-7,0.0000326445,0.002739121,0.000005932831,0.000008627049,7.010022e-7,0.00192804,0.0004539592,0.00003773069,0.1137353,0.0008059521,0.8802515],"study_design_scores_gemma":[0.0002139252,0.00007814147,0.002243585,0.00001107432,0.000003384795,0.00003604038,0.0006635471,0.8331077,0.006986105,0.009786139,0.1465545,0.0003158975],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02642632,0.00006304311,0.9649627,0.0004153899,0.0002635219,0.0001165393,0.00005106432,0.0001736248,0.007527845],"genre_scores_gemma":[0.365045,0.00005402299,0.6339647,0.0006560977,0.0000458792,0.00004793899,0.00008008024,0.00000362618,0.0001025955],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8799357,"threshold_uncertainty_score":0.8393035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0490641143084704,"score_gpt":0.288073862678614,"score_spread":0.2390097483701435,"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."}}