{"id":"W2015047009","doi":"10.1016/j.neunet.2007.04.009","title":"Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: Application to geophysical prospecting","year":2007,"lang":"en","type":"article","venue":"Neural Networks","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Artificial neural network; Computer science; Genetic programming; Artificial intelligence; Set (abstract data type); Principal component analysis; Genetic algorithm; Multi-objective optimization; Pattern recognition (psychology); Data mining; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001755869,0.0003274905,0.0003908966,0.0001816472,0.0006725885,0.0003050359,0.00137912,0.0002092431,0.000004016862],"category_scores_gemma":[0.001232687,0.0003449336,0.0001073954,0.00118325,0.0001198036,0.0008808545,0.0009199189,0.0003866618,0.000001259699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001772172,"about_ca_system_score_gemma":0.00007716921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003080259,"about_ca_topic_score_gemma":0.00002403612,"domain_scores_codex":[0.9961656,0.0001667724,0.0007740944,0.001413902,0.0004981164,0.0009815585],"domain_scores_gemma":[0.9955994,0.001906377,0.0003696577,0.0009807725,0.0008030565,0.000340752],"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":[0.0001505461,0.00009876515,0.0004330054,0.00001114121,0.00002250492,0.000001923473,0.00008859336,0.8780055,0.00002591191,0.0007888522,0.0004697473,0.1199035],"study_design_scores_gemma":[0.0009848078,0.0003169807,0.001201661,0.00001358187,0.00002114516,0.00002097654,0.0001120913,0.9968534,0.00002518775,0.00003446667,0.0000700543,0.0003456115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001486243,0.00004458713,0.993724,0.0004066808,0.001086584,0.002889239,0.000030585,0.0003074923,0.00002460658],"genre_scores_gemma":[0.4220325,0.000002649783,0.5757467,0.0002232713,0.001287576,0.0002173152,0.0004146165,0.00004009263,0.0000352428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4205463,"threshold_uncertainty_score":0.9999003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843541151773481,"score_gpt":0.3534208836653585,"score_spread":0.3049854721476237,"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."}}