{"id":"W2104553574","doi":"10.1109/tip.2006.873472","title":"Pattern generation using likelihood inference for cellular automata","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cellular automaton; Continuous spatial automaton; Stochastic cellular automaton; Inference; Algorithm; Binary number; Automaton; Computer science; Mathematics; Theoretical computer science; Quantum finite automata; Automata theory; Artificial intelligence","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":[0.0001457752,0.0001834852,0.0001401618,0.0001518006,0.0006419673,0.0005790307,0.0004048869,0.00007164141,0.000009731071],"category_scores_gemma":[0.000002482454,0.0001934036,0.00009108855,0.0003943739,0.00004147704,0.0009827418,0.000003753666,0.0001262985,0.00002615473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006966513,"about_ca_system_score_gemma":0.0001421553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007795453,"about_ca_topic_score_gemma":0.00002847524,"domain_scores_codex":[0.9986728,0.00002539483,0.0003050689,0.0004668509,0.0002153399,0.0003145438],"domain_scores_gemma":[0.9991891,0.00004287468,0.0001183501,0.0004362368,0.0001529933,0.00006039221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001251507,0.0002317368,0.000003862277,0.00006081503,0.000005990673,0.000002514372,0.0001242799,0.00547984,0.6146119,0.000109943,0.00007010651,0.3792977],"study_design_scores_gemma":[0.0002268456,0.00001817389,0.00001037655,0.00002443906,0.00001727369,0.000006276743,0.000005936803,0.658792,0.3400703,0.0004715636,0.0001989017,0.0001578547],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01445625,0.00006518817,0.9841281,0.0002759799,0.0001966849,0.0003206347,0.00002064243,0.0004286421,0.0001078981],"genre_scores_gemma":[0.8402081,0.000002653429,0.1593691,0.00009127297,0.0001110308,0.0001079104,0.00001401194,0.00002128602,0.00007459339],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8257518,"threshold_uncertainty_score":0.7886771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02494701860750446,"score_gpt":0.2748644632190721,"score_spread":0.2499174446115676,"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."}}