{"id":"W4408790430","doi":"10.1109/lawp.2025.3554357","title":"Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Communications Research Centre Canada","funders":"","keywords":"Convolutional neural network; Computer science; Path (computing); Artificial intelligence; Artificial neural network; Path loss; Pattern recognition (psychology); Computer network; Telecommunications; Wireless","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":[0.0001065599,0.0001296887,0.0001220175,0.0002306503,0.0001000299,0.00004695628,0.00005685679,0.00006688473,0.000003345488],"category_scores_gemma":[0.000002996378,0.0001365694,0.00003147238,0.0001608347,0.00008733866,0.0002001442,0.00001107106,0.0001489408,8.52794e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007986473,"about_ca_system_score_gemma":0.000007814428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003276704,"about_ca_topic_score_gemma":0.00002260294,"domain_scores_codex":[0.9992933,0.00002893621,0.0002292896,0.0001673196,0.00009999397,0.0001811576],"domain_scores_gemma":[0.9998044,0.00002602706,0.00003045868,0.0000862125,0.00002553903,0.00002735592],"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":[0.0000693392,0.00005188504,0.04451609,0.0003351638,0.00005411079,0.00002444715,0.0001133199,0.9035123,0.03074225,0.0009435189,0.01010844,0.009529124],"study_design_scores_gemma":[0.0005599261,0.00001053066,0.01371021,0.0001332533,0.00001119672,0.000001637088,0.00003755109,0.9844309,0.0007496936,0.00001063999,0.0002331635,0.00011131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4794984,0.00006281406,0.5186234,0.0003678936,0.0005134133,0.000191394,0.000009198848,0.0007043448,0.00002911154],"genre_scores_gemma":[0.9987843,0.00004239007,0.0002781819,0.0007109525,0.00007516199,0.00004668632,0.00003511283,0.00001337322,0.00001384197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5192859,"threshold_uncertainty_score":0.556914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009802138945213788,"score_gpt":0.2011436484112991,"score_spread":0.1913415094660853,"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."}}