{"id":"W7128549648","doi":"10.70102/afts.2025.1834.861","title":"METARFM: A META-LEARNING FRAMEWORK FOR THE ADAPTIVE SELECTION OF RFM MODEL ARIANTS IN CUSTOMER SEGMENTATION","year":2025,"lang":"","type":"article","venue":"Archives for Technical Sciences","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Market segmentation; Transaction data; Database transaction; Segmentation; Robustness (evolution); Scalability; Cluster analysis; Set (abstract data type); Process (computing)","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":[],"consensus_categories":[],"category_scores_codex":[0.001527848,0.0002545967,0.0004673036,0.0006807604,0.0009153657,0.0002463127,0.0005578129,0.0001074964,0.00005020135],"category_scores_gemma":[0.0007363135,0.0001768687,0.0004169828,0.002056568,0.0006877819,0.0009431102,0.0001769107,0.0003019611,0.000003026661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004277511,"about_ca_system_score_gemma":0.0001145704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001669717,"about_ca_topic_score_gemma":0.0002312023,"domain_scores_codex":[0.9978569,0.00005736888,0.0006929624,0.0005756899,0.0003578174,0.000459281],"domain_scores_gemma":[0.9970382,0.002194571,0.0005138951,0.0001395808,0.00009831772,0.00001543067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001036361,0.0004919426,0.003515855,0.0005041305,0.0008287973,2.185937e-7,0.0007283305,0.1198355,0.02081341,0.7688152,0.0003766002,0.08305375],"study_design_scores_gemma":[0.0006830608,0.0001227076,0.002857724,0.0001565612,0.001633017,2.54712e-7,0.001636665,0.7735952,0.001676229,0.2166214,0.0008101665,0.0002069631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007467035,0.000677042,0.9816634,0.002872451,0.0003070571,0.002731578,0.0000147347,0.00005720185,0.004209475],"genre_scores_gemma":[0.9335257,0.0001579486,0.0646056,0.0004811496,0.00009252017,0.000707802,0.00001098773,0.00001302404,0.0004052295],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9260587,"threshold_uncertainty_score":0.7212496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07240980034480188,"score_gpt":0.3372231291829013,"score_spread":0.2648133288380994,"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."}}