{"id":"W4296910914","doi":"10.1109/ap-s/usnc-ursi47032.2022.9887000","title":"Extending Machine Learning Based RF Coverage Predictions to 3D","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Transmitter; Computer science; Power (physics); Work (physics); Artificial intelligence; SIGNAL (programming language); Radio frequency; Machine learning; Data modeling; Training set; Signal processing; Telecommunications; Engineering","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":[0.0003717802,0.0007799028,0.0005029459,0.0006330974,0.0002691307,0.0008208867,0.0008091782,0.0007343993,0.00180129],"category_scores_gemma":[0.003257741,0.0004783223,0.0005560332,0.0004809324,0.0004366469,0.0008920647,0.0007728618,0.0007927794,0.001036397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005064662,"about_ca_system_score_gemma":0.0004577758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007482667,"about_ca_topic_score_gemma":0.006262343,"domain_scores_codex":[0.9997935,0.00004320544,0.000009883483,0.00004996428,0.00008070762,0.00002276865],"domain_scores_gemma":[0.9988958,0.0006494891,0.00009711348,0.0001532636,0.0001760135,0.00002830174],"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":[0.00001577634,0.00001278876,0.0007608656,0.00001139606,0.000008737476,0.00003155546,0.00001429844,0.979022,0.0007902622,0.0004613055,0.0003021526,0.0185688],"study_design_scores_gemma":[9.111172e-7,0.000003387572,0.00008808192,0.000001820734,7.521949e-7,0.000005421332,0.000001797335,0.9989353,0.0002825121,0.0005488325,0.0001289755,0.000002220385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05283709,0.0001611918,0.9410552,0.0002276629,0.00005647491,0.00003258594,0.0003858537,0.002162213,0.003081658],"genre_scores_gemma":[0.8667323,0.0003177172,0.1296004,0.0002074534,0.00006625293,0.0001079998,0.0009416434,0.0003018538,0.001724345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007482667,"threshold_uncertainty_score":0.01487827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456026677315699,"score_gpt":0.2431741338200759,"score_spread":0.2286138670469189,"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."}}