{"id":"W4407051601","doi":"10.1109/twc.2025.3531702","title":"Proactive Handover Type Prediction and Parameter Optimization Based on Machine Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Innovation in Digital Healthcare Systems","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Tibet Autonomous Region; Natural Science Foundation of Inner Mongolia; National Natural Science Foundation of China","keywords":"Computer science; Handover; Artificial intelligence; Machine learning; Computer network","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005023355,0.0002132993,0.0002609091,0.0005362051,0.002296001,0.00003737912,0.0002942355,0.0002815613,0.00008747182],"category_scores_gemma":[0.00009543278,0.0002121518,0.00005529902,0.001086669,0.0001521084,0.0002371785,0.000009723878,0.001449317,0.00005656918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004002943,"about_ca_system_score_gemma":0.0003365403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002442504,"about_ca_topic_score_gemma":0.0001839742,"domain_scores_codex":[0.9974506,0.001014158,0.0006741683,0.0003313289,0.0002360001,0.0002937763],"domain_scores_gemma":[0.9961833,0.001834334,0.0002316711,0.001094245,0.0005780548,0.00007838643],"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":[0.002071178,0.002224914,0.01457751,0.001028156,0.0004777066,0.000001570301,0.004427424,0.8715175,0.0004236383,0.01933614,0.001954697,0.08195951],"study_design_scores_gemma":[0.001467475,0.0002691397,0.00117346,0.0007113226,0.00005197514,9.503646e-7,0.0006072957,0.9892277,0.0003245146,0.000135989,0.005825018,0.0002051693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006221661,0.00008358749,0.9575055,0.00444786,0.001374436,0.002243127,0.0001954771,0.0005133419,0.02741506],"genre_scores_gemma":[0.9910713,0.0001887899,0.002971351,0.001192437,0.00002711405,0.0008570646,0.0001444948,0.00003880812,0.003508676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9848496,"threshold_uncertainty_score":0.9990029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06181393183739644,"score_gpt":0.3804412214516918,"score_spread":0.3186272896142954,"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."}}