{"id":"W2161122238","doi":"10.1142/s021821300600262x","title":"GENERICITY IN EVOLUTIONARY COMPUTATION SOFTWARE TOOLS: PRINCIPLES AND CASE-STUDY","year":2006,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Evolutionary computation; Software engineering; Software; Genetic programming; Evolutionary algorithm; Software development; Evolutionary programming; Computation; Genetic representation; Theoretical computer science; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000513339,0.0001327516,0.0001776471,0.0003233637,0.000125094,0.0003490662,0.0006045225,0.00005108109,0.00001235204],"category_scores_gemma":[0.0001650532,0.0001286467,0.00007135094,0.000350992,0.00007942957,0.001406466,0.0001874014,0.0002067798,0.00001078969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001632175,"about_ca_system_score_gemma":0.000132603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004002503,"about_ca_topic_score_gemma":0.0002145218,"domain_scores_codex":[0.9980414,0.00008842477,0.0009347881,0.0002541205,0.0005150461,0.0001662243],"domain_scores_gemma":[0.998424,0.0003473888,0.0003879959,0.0001386093,0.0006404829,0.00006154866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005571161,0.002246934,0.02048886,0.0000069225,0.00008220722,0.003976652,0.00142755,0.1860197,0.0005686473,0.1031255,0.0002919552,0.6817094],"study_design_scores_gemma":[0.0004075681,0.0006209554,0.1406657,0.0001069973,0.00002922599,0.01362937,0.003786036,0.6795356,0.001457392,0.1576147,0.001505966,0.000640453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4216699,0.0001343304,0.5770057,0.0006385241,0.0003215933,0.0001356945,0.000007719918,0.00002137848,0.00006513854],"genre_scores_gemma":[0.8988336,0.00001993854,0.1007153,0.00005320159,0.0003384093,0.00001164708,0.000005247567,0.00000598178,0.00001666696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.681069,"threshold_uncertainty_score":0.5246061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08307800819345276,"score_gpt":0.3298441421145045,"score_spread":0.2467661339210517,"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."}}