{"id":"W4411171637","doi":"10.1109/tap.2025.3576492","title":"Enhancing Reference Signal Received Power Prediction Accuracy in Wireless Outdoor Settings: A Comprehensive Feature Importance Study","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Wireless; Feature (linguistics); SIGNAL (programming language); Power (physics); Artificial intelligence; Speech recognition; Telecommunications","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":[0.002340077,0.0009097098,0.0008262997,0.0007311897,0.000287568,0.0005329931,0.0005536635,0.0006195481,0.0003844236],"category_scores_gemma":[0.008267199,0.0001914691,0.0004909203,0.0007066759,0.000368277,0.001325457,0.0004762075,0.0007877811,0.000125443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003782218,"about_ca_system_score_gemma":0.0004281531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005848164,"about_ca_topic_score_gemma":0.004078426,"domain_scores_codex":[0.9992684,0.0002351047,0.0000404382,0.0001593494,0.0001863094,0.0001105262],"domain_scores_gemma":[0.9937711,0.004479256,0.0003711443,0.0004993438,0.0007564809,0.0001225456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002886208,0.0002571994,0.03566408,0.00006301632,0.00008594027,0.000163896,0.00004536852,0.8737344,0.003311102,0.0004437051,0.0009149612,0.08502766],"study_design_scores_gemma":[0.000003392949,0.00006396295,0.005549174,0.00000476673,0.00001369714,0.00002881854,0.000009131402,0.993016,0.00101241,0.0001894424,0.0001035383,0.000005683069],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7881984,0.001009632,0.2081517,0.000334138,0.00007626929,0.0000467897,0.0002091039,0.0005356365,0.001438333],"genre_scores_gemma":[0.9894644,0.0001353938,0.009920319,0.00002858677,0.00003137688,0.000009602822,0.0001977211,0.00001834816,0.0001943148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005848164,"threshold_uncertainty_score":0.01237565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107761704205128,"score_gpt":0.2448747339844293,"score_spread":0.2340985635639165,"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."}}