{"id":"W4411215333","doi":"10.1007/978-981-96-6468-9_25","title":"Optimizing Remote Medical Services with AloT: Integration of Large Language Models and 6G Edge Computing","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; York University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0003383184,0.0001262108,0.00018287,0.0004731166,0.0001366076,0.00007727075,0.0007488133,0.0001119186,0.00000149608],"category_scores_gemma":[0.00001555376,0.0001111707,0.00001100415,0.0001951119,0.0003729572,0.002268511,0.0008489504,0.0003158293,8.051941e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003655165,"about_ca_system_score_gemma":0.00004855047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001017737,"about_ca_topic_score_gemma":0.00003831777,"domain_scores_codex":[0.9992206,0.000005867895,0.0003570046,0.0001039643,0.0002001867,0.0001124066],"domain_scores_gemma":[0.9990131,0.00009428047,0.0001035568,0.0006435517,0.0001130594,0.0000325111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002772292,0.000005238369,0.000007549286,0.0002746166,0.00001022648,3.789961e-7,0.005208385,0.009939187,0.00001426957,0.1901751,0.00003784225,0.7943244],"study_design_scores_gemma":[0.0001551862,0.00001472519,0.00003710361,0.0009671026,0.000004460786,0.000006448511,0.0002923717,0.9940777,0.00004292726,0.001131392,0.003158244,0.0001122935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006386106,0.001356905,0.9463303,0.00009691774,0.00005472524,0.000161563,0.00002891866,0.0001492715,0.05118276],"genre_scores_gemma":[0.4098306,0.01252931,0.5768872,0.0003101872,0.00001832224,0.000004278035,0.0002762363,0.00001388181,0.0001299054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9841385,"threshold_uncertainty_score":0.453341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878014809728152,"score_gpt":0.2868294432917256,"score_spread":0.2680492951944441,"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."}}