{"id":"W4388203515","doi":"10.18280/mmep.100512","title":"Machine Learning Approach to Users’ Age Prediction: A Telecom Company Case Study in Saudi Arabia","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Telecommunications; Computer science; Business","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":[0.0009915786,0.0001589387,0.0002794207,0.0002855127,0.0002345817,0.00008162433,0.0001452609,0.0001191873,0.000003393606],"category_scores_gemma":[0.000133835,0.0001575673,0.00002890815,0.0007862876,0.00004797262,0.00008273907,0.00008273582,0.0004205492,0.00002065912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005224881,"about_ca_system_score_gemma":0.0000120123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006860711,"about_ca_topic_score_gemma":0.0001639549,"domain_scores_codex":[0.9986421,0.00005514871,0.0003052833,0.0003273085,0.0002286637,0.0004414458],"domain_scores_gemma":[0.9994967,0.0001506345,0.00002650542,0.0001583892,0.00002052362,0.000147267],"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":[0.000002024083,0.0001514569,0.001539735,0.0001472506,0.00001720462,0.0001769195,0.05508402,0.9400033,0.00001170664,0.002575744,0.0000119646,0.000278723],"study_design_scores_gemma":[0.0003045619,0.00006327041,0.0001314489,0.000126275,0.0000130281,0.00006454129,0.004560017,0.9922032,0.000001502103,0.002014854,0.0003467836,0.0001705575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8226888,0.0000458782,0.174742,0.0001652151,0.00004709967,0.0006712179,0.000001913099,0.001063568,0.000574344],"genre_scores_gemma":[0.9880785,0.00002644276,0.01142955,0.000005735433,0.00003122949,0.0001507564,0.000002565353,0.00002910397,0.0002460537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1653898,"threshold_uncertainty_score":0.6425409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04770684056488466,"score_gpt":0.2612897122947379,"score_spread":0.2135828717298532,"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."}}