{"id":"W2059499379","doi":"10.1080/03081070290018056","title":"Statistical learning theory, model identification and system information content","year":2002,"lang":"en","type":"article","venue":"International Journal of General Systems","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"","keywords":"Computer science; Statistical learning theory; Mathematical theory; Simple (philosophy); Expression (computer science); Statistical theory; Probability theory; Identification (biology); Statistical model; Information theory; Artificial intelligence; Algorithmic learning theory; Learning theory; Management science; Industrial engineering; Machine learning; Mathematics; Active learning (machine learning); Engineering; Support vector machine","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":[0.00115573,0.0001104266,0.0001947648,0.0002131153,0.00007444176,0.0006289658,0.0006599088,0.0000495595,0.00000548415],"category_scores_gemma":[0.0001730725,0.00009219273,0.00005875954,0.00008021941,0.00003625133,0.001279391,0.0001399105,0.0001761108,0.00002487405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002424249,"about_ca_system_score_gemma":0.00002861257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003018849,"about_ca_topic_score_gemma":5.766475e-7,"domain_scores_codex":[0.9978806,0.0002250556,0.0008534448,0.0001367473,0.0007709577,0.000133246],"domain_scores_gemma":[0.998072,0.0001189187,0.0006305862,0.0001396176,0.0009343221,0.0001045727],"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":[0.00003486863,0.00008770422,0.001462412,0.00006874024,0.0001817474,0.00004614579,0.002024038,0.2048824,0.001079108,0.7049186,0.001138243,0.08407597],"study_design_scores_gemma":[0.0003884317,0.00006765367,0.001425801,0.00004848952,0.000008465258,0.0007246489,0.0001831799,0.9949559,0.00004848275,0.001185203,0.0008691585,0.00009457622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03411189,0.0002649986,0.9621814,0.0004286031,0.002179598,0.0001122126,0.000008015625,0.00004422451,0.0006690903],"genre_scores_gemma":[0.9869288,0.00002815356,0.01233289,0.00004264853,0.0003164485,0.000005022006,0.000003648887,0.000004695422,0.0003377196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9528169,"threshold_uncertainty_score":0.6065133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248984344631063,"score_gpt":0.2473958668784426,"score_spread":0.2149060234321319,"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."}}