{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007227048,0.002937156,0.002737775,0.00383262,0.0009654504,0.002475175,0.005235587,0.003106902,0.002651889],"category_scores_gemma":[0.01401567,0.001385652,0.003578823,0.002245565,0.001197158,0.00340452,0.002390285,0.003525195,0.001547217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002286311,"about_ca_system_score_gemma":0.002735603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084697,"about_ca_topic_score_gemma":0.01491239,"domain_scores_codex":[0.9975483,0.001029795,0.000147495,0.0007636659,0.000298997,0.0002118504],"domain_scores_gemma":[0.9955248,0.00295866,0.0003406313,0.000499038,0.0004774097,0.000199483],"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":[0.0003988463,0.0003553145,0.009248046,0.0002286488,0.0007845549,0.0001846069,0.0002832452,0.7546183,0.002409698,0.006390878,0.006702764,0.2183951],"study_design_scores_gemma":[0.00001770951,0.00004540783,0.0002409092,0.000023759,0.00004164076,0.00002503219,0.00001876928,0.9931652,0.0005018449,0.005236518,0.0006674299,0.00001581684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02543215,0.001973982,0.9635584,0.0008364879,0.0001004468,0.0001696649,0.0007762858,0.005638021,0.001514724],"genre_scores_gemma":[0.4418172,0.0008002228,0.5475655,0.001440366,0.0002739454,0.0007055257,0.003442659,0.001053282,0.002901272],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01084697,"threshold_uncertainty_score":0.03822076,"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."}}