{"id":"W2085047719","doi":"10.1109/sis.2014.7011810","title":"Improving artifact selection via agent migration in multi-population cultural algorithms","year":2014,"lang":"en","type":"article","venue":"","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Science Foundation","keywords":"Artifact (error); Selection (genetic algorithm); Computer science; Population; Artificial intelligence; Machine learning; Sociology","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":[0.002677929,0.0009405095,0.0007822549,0.0007750453,0.0007892773,0.001233279,0.001485984,0.001134012,0.0009895043],"category_scores_gemma":[0.01114245,0.0002839649,0.0006652051,0.0005122357,0.0009337555,0.001315893,0.001815984,0.0008475298,0.0002178194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006940346,"about_ca_system_score_gemma":0.001013167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002435897,"about_ca_topic_score_gemma":0.002382559,"domain_scores_codex":[0.9987948,0.000719285,0.00006228577,0.000142664,0.0001906656,0.00009035189],"domain_scores_gemma":[0.9959603,0.002533777,0.0004166697,0.0004495002,0.0004759646,0.0001637991],"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":[0.0001162201,0.0002036237,0.008000104,0.0001007295,0.0001336178,0.0001977083,0.0004633059,0.8841761,0.004110389,0.01408961,0.0005184364,0.08789],"study_design_scores_gemma":[0.00003007137,0.0001292303,0.0004051237,0.00001506143,0.00003699494,0.00005574883,0.00008740029,0.9920277,0.001318619,0.005028468,0.0008551269,0.00001050419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2563927,0.0004364531,0.7365196,0.0004499665,0.00007045339,0.0002106568,0.00003268239,0.0003546163,0.005532918],"genre_scores_gemma":[0.8350564,0.0001868258,0.1626955,0.0001462327,0.0000246593,0.0001813849,0.00005663943,0.00004262829,0.00160977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002677929,"threshold_uncertainty_score":0.01416242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461440956369284,"score_gpt":0.311646721137932,"score_spread":0.2870323115742392,"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."}}