{"id":"W2473747158","doi":"10.22215/etd/2008-06305","title":"Automatic rule discovery and generalization in supervised and unsupervised learning tasks","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Generalization; Computer science; Artificial intelligence; Information retrieval; Natural language processing; Machine learning; Mathematics","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":[0.0001679668,0.0002485547,0.0003908012,0.0002135649,0.0001510225,0.0003335873,0.0002570254,0.0002163604,0.00000644174],"category_scores_gemma":[0.00003908377,0.000214029,0.00004064617,0.0002486504,0.00002252196,0.0006607749,0.00005807169,0.0001933166,0.000006068441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003305462,"about_ca_system_score_gemma":0.00008762847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005650842,"about_ca_topic_score_gemma":0.0003003895,"domain_scores_codex":[0.9984982,0.0001494312,0.0003685687,0.000503115,0.0002463204,0.0002343727],"domain_scores_gemma":[0.999483,0.00006859327,0.0001084623,0.0002272822,0.00004361106,0.00006905061],"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":[0.0001672258,0.0005345877,0.1200946,0.004466845,0.0003690638,0.000481552,0.066489,0.00254087,0.01658634,0.1779496,0.00206595,0.6082544],"study_design_scores_gemma":[0.00244596,0.0001627076,0.1672166,0.0004168258,0.00002860176,0.0000553979,0.001633587,0.8228142,0.0001618919,0.003848851,0.0002416219,0.0009738089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819499,0.002594132,0.004315657,0.00008465686,0.0002940941,0.0003773559,0.000001218644,0.000173087,0.01020985],"genre_scores_gemma":[0.9901853,0.0007484868,0.001688489,0.0001097285,0.0000532599,0.0000563638,0.0001737595,0.0000200178,0.006964561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8202733,"threshold_uncertainty_score":0.8727849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0092851049577769,"score_gpt":0.2156350112545302,"score_spread":0.2063499062967533,"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."}}