{"id":"W3008120122","doi":"10.5539/ibr.v13n3p106","title":"Latent Variable Models for Integrated Analysis of Credit and Point Usage History Data on Rewards Credit Card System","year":2020,"lang":"en","type":"article","venue":"International Business Research","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Credit card; Purchasing; Point of sale; Credit card interest; Point (geometry); Diversity (politics); Function (biology); Credit history; Business; Computer science; Marketing; Finance; World Wide Web; Payment; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001130504,0.0001378147,0.0003057133,0.001022078,0.00008828216,0.0001897741,0.000700813,0.00006725112,0.0002371329],"category_scores_gemma":[0.0006445489,0.0001247992,0.00006119032,0.001436497,0.00008575875,0.001140359,0.0004844193,0.0001687842,0.00002519841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003199979,"about_ca_system_score_gemma":0.0001093818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002965428,"about_ca_topic_score_gemma":0.00007736987,"domain_scores_codex":[0.9978973,0.00003200259,0.0003860815,0.0004899026,0.0009745718,0.0002201208],"domain_scores_gemma":[0.9971913,0.0001787915,0.0001737637,0.0003357387,0.002089832,0.00003064953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006854007,0.001111706,0.02532469,0.00559842,0.008153999,0.0001197725,0.001553315,0.1399553,0.0189505,0.1649152,0.6116825,0.0157806],"study_design_scores_gemma":[0.000728866,0.0000206725,0.007635587,0.0001212214,0.0002650143,6.855581e-7,0.0003610651,0.9186046,0.00003625721,0.0002669466,0.07180871,0.0001503957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4730073,0.001402785,0.3374144,0.03550128,0.008236843,0.005080744,0.004902362,0.0007850807,0.1336693],"genre_scores_gemma":[0.9944636,0.00003116857,0.0006358842,0.0004281866,0.0009780874,0.00005155865,0.002896302,0.00002673268,0.0004884669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7786493,"threshold_uncertainty_score":0.5089164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1827138472993647,"score_gpt":0.3409923829700576,"score_spread":0.158278535670693,"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."}}