{"id":"W2133956363","doi":"10.1007/978-3-540-39869-1_18","title":"Improving Evolutionary Learning of Cooperative Behavior by Including Accountability of Strategy Components","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Accountability; Computer science; Evolutionary algorithm; Artificial intelligence; Action (physics); Process (computing); Evolutionary computation; Measure (data warehouse); Machine learning; Data mining","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":[0.0006971341,0.0003603473,0.0005179343,0.0003547841,0.0003412458,0.00008244106,0.001713827,0.0002304965,0.00001931446],"category_scores_gemma":[0.00006304224,0.0003579715,0.0001114401,0.0006227399,0.0009420969,0.0006373446,0.0008904117,0.0007055193,0.00000336689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003239211,"about_ca_system_score_gemma":0.0004818733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000170332,"about_ca_topic_score_gemma":0.00001159439,"domain_scores_codex":[0.9969542,0.00006604298,0.0007377715,0.001051099,0.0008046749,0.0003862359],"domain_scores_gemma":[0.9975777,0.0003600567,0.0006018528,0.0007716331,0.0005883458,0.0001003953],"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":[0.00002123763,0.0009782597,0.004430099,0.0002988716,0.00006285933,0.00002170247,0.002005364,0.270831,0.06936593,0.09117073,0.0001099028,0.5607041],"study_design_scores_gemma":[0.0005016759,0.0004570589,0.002594287,0.0003447621,0.00002737794,0.0000548476,0.00000338,0.9633388,0.008858956,0.02249038,0.0004562942,0.0008721864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003819234,0.0006530334,0.9941424,0.00007124006,0.0002484797,0.0004540342,0.00003251015,0.00004801066,0.0005311034],"genre_scores_gemma":[0.8052263,0.00003352418,0.1944126,0.00008114958,0.00006021643,0.0000255361,0.00002633903,0.00001993063,0.0001144636],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.801407,"threshold_uncertainty_score":0.9998872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667616143080469,"score_gpt":0.2755858887287938,"score_spread":0.2489097272979891,"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."}}