{"id":"W2198861419","doi":"","title":"Emergence and induction of cellular automata rules via probabilistic reinforcement paradigms: Research Articles","year":2006,"lang":"en","type":"article","venue":"Complexity","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Probabilistic logic; Simple (philosophy); Process (computing); Cellular automaton; Automaton; Class (philosophy); Noise (video); Selection (genetic algorithm); Binary number; Algorithm; Theoretical computer science; Stochastic process; Artificial intelligence; Mathematics; Statistics; Arithmetic","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001592745,0.0003873941,0.0004433268,0.000664145,0.0005015277,0.001673843,0.0009997698,0.001144184,0.0021803],"category_scores_gemma":[0.009107769,0.0003518952,0.0008106997,0.0006854028,0.002844697,0.002810485,0.001071704,0.00176391,0.0004589203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131591,"about_ca_system_score_gemma":0.00079578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120421,"about_ca_topic_score_gemma":0.0008624648,"domain_scores_codex":[0.9988891,0.0004274804,0.00006039416,0.0002321975,0.0003232649,0.00006755405],"domain_scores_gemma":[0.9943463,0.004134118,0.0003963246,0.0005783534,0.0004137401,0.0001311753],"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":[0.00003886997,0.00006389547,0.001857683,0.0002132841,0.00004342011,0.0001372467,0.0004149077,0.07931466,0.002450613,0.8320725,0.001974898,0.08141797],"study_design_scores_gemma":[0.00001871983,0.0000372355,0.0006518848,0.00004844989,0.00001325525,0.000125961,0.00006460056,0.2658641,0.001781611,0.7200478,0.01132192,0.0000244569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05675052,0.004066907,0.9091594,0.003826358,0.0002260123,0.00008328159,0.00009617005,0.0003686624,0.02542282],"genre_scores_gemma":[0.8170592,0.00427946,0.1696443,0.0004661875,0.0004391408,0.0001994735,0.0001770601,0.0001292384,0.00760597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0021803,"threshold_uncertainty_score":0.009547591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1051761654037827,"score_gpt":0.3149025345211712,"score_spread":0.2097263691173885,"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."}}