{"id":"W2028913248","doi":"10.1109/ia.2014.7009455","title":"Na&amp;#x00EF;ve creature learns to cross a highway in a simulated CA-like environment","year":2014,"lang":"en","type":"article","venue":"","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Creatures; Computer science; Population; Process (computing); Artificial intelligence; Base (topology); Human–computer interaction; Natural (archaeology); Sociology; Geography","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":[0.0002219327,0.0002814077,0.0003376152,0.0002699889,0.0004378479,0.0005970166,0.0009703111,0.0009866938,0.004020719],"category_scores_gemma":[0.001626054,0.0001752792,0.0003544755,0.0002502703,0.0008794076,0.0008012383,0.0004964434,0.0005315586,0.0002634284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007155304,"about_ca_system_score_gemma":0.0006458413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01814367,"about_ca_topic_score_gemma":0.01101655,"domain_scores_codex":[0.9999014,0.00002834345,0.000004168652,0.00002700564,0.00001378709,0.00002517418],"domain_scores_gemma":[0.9992895,0.0003321249,0.0001088189,0.00008583185,0.00007444857,0.0001092607],"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":[0.00008105199,0.0000636959,0.002833114,0.00002254903,0.00002782933,0.000121517,0.0000820146,0.9825699,0.002010073,0.009141541,0.0003328974,0.002713865],"study_design_scores_gemma":[0.00002057418,0.00004962097,0.0003607913,0.000003814674,0.000008135029,0.00002050847,0.00003710886,0.9964327,0.0004968863,0.002218969,0.0003448309,0.000006043838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9050034,0.00005389723,0.0822362,0.0003535147,0.00003376601,0.00005395349,0.0002315072,0.0003189992,0.01171479],"genre_scores_gemma":[0.9878115,0.0000407288,0.009099533,0.00003201557,0.000003050186,0.00006159792,0.00007941814,0.00001053949,0.002861586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01814367,"threshold_uncertainty_score":0.03607613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025979770954227,"score_gpt":0.2485725845104829,"score_spread":0.2383127868009407,"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."}}