{"id":"W1483633733","doi":"10.1201/9780203911570-14","title":"Econometric Modeling Based on Pattern Recognition via the Fuzzy C-Means Clustering Algorithm","year":2003,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Fuzzy logic; Cluster analysis; Context (archaeology); Kernel regression; Fuzzy clustering; Kernel (algebra); Data mining; Econometric model; Model selection; Machine learning; Artificial intelligence; Econometrics; Algorithm; Pattern recognition (psychology); Nonparametric statistics; Mathematics","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":[0.004119547,0.0004474996,0.0006178101,0.00104602,0.0003372784,0.0006801902,0.002288945,0.0004577925,0.00001684506],"category_scores_gemma":[0.0002089272,0.0003914524,0.0002963812,0.0004223489,0.000103054,0.000213756,0.001093946,0.002006993,0.00007107968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230966,"about_ca_system_score_gemma":0.0004435043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002537112,"about_ca_topic_score_gemma":0.0001445615,"domain_scores_codex":[0.9952629,0.0008549128,0.0009052897,0.001498606,0.0004466087,0.001031728],"domain_scores_gemma":[0.9964722,0.0008374067,0.0002740512,0.002045625,0.000161761,0.0002089348],"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":[0.000008861169,0.00006001554,0.00006967072,0.00004468208,0.00002656513,0.00001566003,0.00008127304,0.3357572,0.000001318648,0.000043946,0.000008191284,0.6638826],"study_design_scores_gemma":[0.0005776792,0.0001093906,0.0001046809,0.0001779159,0.000004917693,0.00001111417,0.00009866384,0.9908847,0.000004906286,0.007135354,0.0004644311,0.0004263028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01076368,0.0004688638,0.6230294,0.002252479,0.004057922,0.003512463,0.0001156718,0.0002787001,0.3555208],"genre_scores_gemma":[0.9853613,0.001321712,0.01067337,0.0009124929,0.0005085113,0.0008216277,0.00005613609,0.0000813661,0.0002635038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9745976,"threshold_uncertainty_score":0.9998537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04923190937110425,"score_gpt":0.275347391023125,"score_spread":0.2261154816520208,"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."}}