{"id":"W2012068092","doi":"10.1145/2001858.2002106","title":"Bayesian networks learning for strategies in artificial life","year":2011,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian network; Artificial intelligence; Artificial life; Computer science; Machine learning; Process (computing); Bayesian probability; Representation (politics); Graphical model; Evolutionary algorithm; Evolutionary computation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002815189,0.00009236094,0.0001121502,0.00006572293,0.00007378087,0.0001513745,0.0003819659,0.00007146548,0.00003061407],"category_scores_gemma":[0.00003021959,0.00008399674,0.00003724677,0.0001854949,0.00002187478,0.0003891471,0.00005962385,0.0001581882,0.00001163241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008766977,"about_ca_system_score_gemma":0.00009494522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001178378,"about_ca_topic_score_gemma":0.0001196276,"domain_scores_codex":[0.9991452,0.00003767434,0.000201535,0.0002589567,0.00007304961,0.0002836538],"domain_scores_gemma":[0.9996091,0.00004947113,0.00003843645,0.0001798759,0.00004069262,0.00008244896],"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":[0.000013925,0.00004664988,0.0006087464,0.000005854382,0.000004911039,0.000003703726,0.001124259,0.02806664,0.00005010068,0.927318,0.0001489125,0.04260831],"study_design_scores_gemma":[0.00007601974,0.000079317,0.0005514635,0.000008844415,0.000001205642,9.550313e-7,0.0002068375,0.929574,0.0001267213,0.06919134,0.00006240093,0.0001209033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002550344,0.00003339198,0.9858262,0.0001254043,0.0001549697,0.00008691117,7.288327e-8,0.0001668922,0.01105579],"genre_scores_gemma":[0.9247855,0.000004481726,0.07483792,0.0001735464,0.00006009761,0.00001936869,7.210367e-7,0.000005708062,0.0001126556],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9222351,"threshold_uncertainty_score":0.3425288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06801594693876549,"score_gpt":0.2692295468228511,"score_spread":0.2012135998840855,"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."}}