{"id":"W2976677836","doi":"10.1109/cig.2019.8847947","title":"Automatic Generation of Diverse Cavern Maps with Morphing Cellular Automata","year":2019,"lang":"en","type":"article","venue":"2019 IEEE Conference on Games (CoG)","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Cellular automaton; Computer science; Automaton; Morphing; Mobile automaton; Function (biology); Theoretical computer science; Fitness function; Stochastic cellular automaton; Automata theory; Algorithm; Artificial intelligence; Machine learning; Genetic algorithm","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":[0.0003165624,0.0003911052,0.0004261085,0.0005271289,0.0003928199,0.0006274694,0.0007494657,0.0004089345,0.002250671],"category_scores_gemma":[0.001216407,0.000273119,0.0006153367,0.0003438806,0.0005113924,0.0004739451,0.001039808,0.0004325745,0.0003573854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004013075,"about_ca_system_score_gemma":0.000268412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001589612,"about_ca_topic_score_gemma":0.002137358,"domain_scores_codex":[0.9998457,0.00002736874,0.000009799884,0.00004166172,0.00005270906,0.00002275095],"domain_scores_gemma":[0.9996063,0.000176039,0.00002353151,0.00008665204,0.00007483827,0.00003276896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001448577,0.00009516478,0.004162264,0.0001716963,0.00008745712,0.0007120033,0.0006581228,0.625254,0.08288357,0.06742703,0.003944608,0.2144593],"study_design_scores_gemma":[0.00001396275,0.00003502597,0.0005296025,0.000008789416,0.00001417486,0.0001168503,0.00005027163,0.9785281,0.007086422,0.01012741,0.003472263,0.00001712902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1830864,0.0001175546,0.7999227,0.0001193605,0.00009174673,0.0001599458,0.000134691,0.002238577,0.01412906],"genre_scores_gemma":[0.7122254,0.00009320324,0.2821347,0.0000642353,0.000009257045,0.0001446353,0.0001956005,0.0002842914,0.004848678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002250671,"threshold_uncertainty_score":0.007529259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616004166228312,"score_gpt":0.2377961969365766,"score_spread":0.2016361552742935,"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."}}