{"id":"W4394773582","doi":"10.1109/lawp.2024.3388253","title":"Machine Learning-Based Path Loss Modeling With Simplified Features","year":2024,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Path (computing); Artificial intelligence; Machine learning; Programming language","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":[0.00008018538,0.0002002528,0.0001492733,0.0001117831,0.0001105116,0.0001558776,0.00006084548,0.00005203023,0.00000340437],"category_scores_gemma":[0.000003203309,0.0001487026,0.00003631195,0.0001486747,0.00004394364,0.00018182,0.000006543709,0.0003456531,0.00000463666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003787965,"about_ca_system_score_gemma":0.00001564221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002920999,"about_ca_topic_score_gemma":0.000005937658,"domain_scores_codex":[0.9992028,0.00001474816,0.0001455303,0.0002264645,0.0001516822,0.0002587943],"domain_scores_gemma":[0.9997706,0.0000208003,0.00001790305,0.00009718144,0.00003945179,0.00005409821],"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":[0.00004838989,0.000005167164,0.0004986791,0.0004940433,0.00006631966,0.0001766376,0.0004074358,0.8627341,0.121362,0.0003367519,0.0002304976,0.01364003],"study_design_scores_gemma":[0.0002441994,0.00003793762,0.0002199147,0.0003759698,0.00002217296,0.00003434001,0.00004418198,0.989167,0.009231679,0.000036671,0.0003399791,0.0002459859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6705064,0.000347269,0.3276903,0.0005153955,0.0003604841,0.000116868,0.000005058317,0.0004098378,0.00004847737],"genre_scores_gemma":[0.9988691,0.0001060937,0.0003235528,0.0002938919,0.0002839058,0.00002177608,0.0000198757,0.00005512093,0.00002664542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3283628,"threshold_uncertainty_score":0.6063916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005196617302316667,"score_gpt":0.1926599269314869,"score_spread":0.1874633096291702,"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."}}