{"id":"W4417404700","doi":"10.1109/asiancon66527.2025.11281203","title":"User Behaviour Analysis to Detect Prospective Customers Using Cyper Physical Systems","year":2025,"lang":"","type":"article","venue":"","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":"Plan (archaeology); Customer base; Channel (broadcasting); Service (business); Android (operating system); Online and offline; Task (project management); Customer service","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":[0.0006012457,0.000397707,0.0003540868,0.002834359,0.0003927546,0.001007961,0.0003362581,0.0003315655,0.003102313],"category_scores_gemma":[0.002904086,0.0001630575,0.0002507905,0.001317268,0.0001838044,0.0007066405,0.0004138253,0.0003474341,0.001102862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004628592,"about_ca_system_score_gemma":0.000275708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00637408,"about_ca_topic_score_gemma":0.00715029,"domain_scores_codex":[0.9993207,0.0002044786,0.00006651897,0.0001409257,0.0001854358,0.00008188607],"domain_scores_gemma":[0.9977081,0.0009515984,0.0003736268,0.0002441152,0.0005761515,0.000146434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007379188,0.0007276312,0.7635753,0.00021713,0.000116572,0.0003458307,0.003068815,0.003167617,0.01568337,0.001103267,0.002997796,0.2082587],"study_design_scores_gemma":[0.00001258195,0.0008444845,0.9103849,0.00003785282,0.00006260617,0.0004506007,0.002325576,0.07613116,0.00607232,0.0004791501,0.003143319,0.00005544125],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759241,0.00009740187,0.01362834,0.0001162935,0.0000141683,0.0003009944,0.001077942,0.00128595,0.007554784],"genre_scores_gemma":[0.9901839,0.00004675629,0.007418334,0.00002908937,0.000006065188,0.00008866812,0.0004419429,0.0000217059,0.001763669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00637408,"threshold_uncertainty_score":0.01267397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405021805532186,"score_gpt":0.2812685300489355,"score_spread":0.2672183119936136,"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."}}