{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008858121,0.001099425,0.0009661088,0.0006111932,0.0004438493,0.0005836678,0.001040433,0.0007739899,0.0006853],"category_scores_gemma":[0.002894329,0.0003602672,0.0005095903,0.0003706302,0.0004746158,0.0007513923,0.0006415206,0.001170348,0.000192983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007231468,"about_ca_system_score_gemma":0.001107721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007313886,"about_ca_topic_score_gemma":0.005976357,"domain_scores_codex":[0.9994825,0.0001101506,0.00003942839,0.0001446222,0.0001308351,0.00009246875],"domain_scores_gemma":[0.9988196,0.0005929996,0.0001719138,0.0000821467,0.0002733631,0.00005988137],"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":[0.0001325593,0.0001874152,0.00463939,0.00004490987,0.00005545957,0.00009189755,0.00006228771,0.8774348,0.003797205,0.0009124724,0.001058975,0.1115827],"study_design_scores_gemma":[0.000004551204,0.00001781659,0.0002153314,0.000001643352,0.000004052444,0.000007646951,0.000002819696,0.9990951,0.00036703,0.0002398858,0.00004116152,0.000002980002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08944097,0.0004795837,0.9068442,0.0002667702,0.00007832543,0.00008129657,0.00006003873,0.001011379,0.001737456],"genre_scores_gemma":[0.9581614,0.0001085737,0.04043556,0.0001198011,0.0000423991,0.00007419972,0.000109368,0.00002575494,0.0009229766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007313886,"threshold_uncertainty_score":0.01454264,"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."}}