{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003851342,0.0005501868,0.0008761077,0.002628777,0.0005602856,0.001698651,0.0003661641,0.000160092,0.0004720844],"category_scores_gemma":[0.0000592009,0.0005289569,0.0005841443,0.009127609,0.00007615487,0.001425175,0.000386228,0.000277754,0.0006599436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005512179,"about_ca_system_score_gemma":0.00008451169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005691309,"about_ca_topic_score_gemma":0.0004894319,"domain_scores_codex":[0.9969101,0.00004940949,0.0006805606,0.001010766,0.0006437454,0.0007054324],"domain_scores_gemma":[0.9984334,0.00005526313,0.0002870809,0.000532767,0.0006310811,0.00006046511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005794252,0.001229133,0.8727714,0.001076328,0.01056289,0.00005702503,0.001546704,0.0640493,0.01241096,0.01969177,0.007514986,0.008510034],"study_design_scores_gemma":[0.00378611,0.00008990522,0.3917101,0.0005591653,0.04452389,0.000002664575,0.01905272,0.5203493,0.002332037,0.0002283599,0.01443989,0.002925863],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.948777,0.0002040274,0.02102512,0.0003385153,0.001731976,0.00178574,0.00001159852,0.0001834251,0.02594259],"genre_scores_gemma":[0.9853265,0.000006384584,0.000214115,0.001002918,0.0007912551,0.00007809974,0.00002545942,0.00004583299,0.01250949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4810614,"threshold_uncertainty_score":0.9997162,"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."}}