{"id":"W4294811494","doi":"10.1109/cec55065.2022.9870271","title":"Mixed Media in Evolutionary Art","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Congress on Evolutionary Computation (CEC)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Computer graphics (images); Bitmap; Variety (cybernetics); Stylized fact; Digital art; Genetic programming; Pixel; Image (mathematics); Object (grammar); Computer vision; Artificial intelligence; Simple (philosophy); Media arts; Digital media; Art; Visual arts; World Wide Web","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":[0.001230458,0.0007900805,0.0005711167,0.001259001,0.001342945,0.004276168,0.001217452,0.001870136,0.01329402],"category_scores_gemma":[0.005180076,0.0004121855,0.0007715645,0.001139028,0.003564092,0.003661097,0.003218579,0.001804585,0.001392659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430805,"about_ca_system_score_gemma":0.0004875603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009726505,"about_ca_topic_score_gemma":0.001113353,"domain_scores_codex":[0.998982,0.0004391359,0.00004952086,0.0001825896,0.0002778527,0.00006886637],"domain_scores_gemma":[0.9983093,0.001081838,0.0001096816,0.0002437132,0.0001689692,0.00008643057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000455041,0.00003710486,0.0004535256,0.000265599,0.00003457771,0.0001644519,0.0003849719,0.03446027,0.002631742,0.8936402,0.002313693,0.06556835],"study_design_scores_gemma":[0.0000388962,0.0001018147,0.0005813084,0.0002238687,0.00003895682,0.0005271262,0.0002040694,0.1263896,0.00372955,0.777599,0.09050189,0.00006395402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0266684,0.0124936,0.7762807,0.002421425,0.0008017072,0.0001653231,0.0001454773,0.0005986695,0.1804247],"genre_scores_gemma":[0.5803319,0.005500228,0.3631147,0.001119445,0.0005393321,0.0005149638,0.0002293637,0.0005034631,0.04814649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01329402,"threshold_uncertainty_score":0.04447287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02323108589386139,"score_gpt":0.250654001202075,"score_spread":0.2274229153082137,"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."}}