{"id":"W4297201455","doi":"10.3390/jrfm15100424","title":"Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Computer science; Cluster analysis; Mixture model; Machine learning; Python (programming language); Artificial intelligence; Multilayer perceptron; Perceptron; Personalization; Market segmentation; k-means clustering; Algorithm; Recommender system; Artificial neural network; Marketing; World Wide Web","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":[0.000790555,0.00008489314,0.0001913112,0.0001085863,0.0004577158,0.00004084683,0.0001154274,0.00004804596,0.000006244524],"category_scores_gemma":[0.00009276977,0.00008139526,0.00004604243,0.0001227674,0.0001952344,0.000127808,0.0001428436,0.0001985895,2.206867e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003989929,"about_ca_system_score_gemma":0.00003831878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002105857,"about_ca_topic_score_gemma":0.00008211633,"domain_scores_codex":[0.9990497,0.00009942055,0.0002483935,0.0001324054,0.0003223755,0.0001476778],"domain_scores_gemma":[0.9994688,0.00004289981,0.0002550069,0.00006935828,0.00005561309,0.0001082998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005612297,0.0001610159,0.007408722,0.00002547792,0.00001119121,0.00001298379,0.00940218,0.00001858579,0.00001965444,0.002549265,0.00031089,0.9800239],"study_design_scores_gemma":[0.006522963,0.0008684264,0.5730081,0.0001603245,0.001170058,0.00004586564,0.090812,0.008631394,0.00001104044,0.02197394,0.2957694,0.001026452],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733991,0.002398857,0.02161097,0.0003138027,0.0002587145,0.0004226235,0.00006765744,0.00001246283,0.001515821],"genre_scores_gemma":[0.9893599,0.004298861,0.005947306,0.00005167537,0.0001000354,0.00002106364,0.000001814535,0.000007263088,0.0002121109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9789975,"threshold_uncertainty_score":0.3520426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01160047215805159,"score_gpt":0.2642469811211656,"score_spread":0.252646508963114,"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."}}