{"id":"W4384637631","doi":"10.2528/pier23032403","title":"Remote Material Characterization with Complex Baseband FMCW Radar Sensors","year":2023,"lang":"en","type":"article","venue":"Electromagnetic waves","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"CMC Microsystems (Canada); University of Waterloo","funders":"","keywords":"Baseband; Remote sensing; Continuous-wave radar; Radar; Characterization (materials science); Computer science; Geology; Materials science; Radar imaging; Telecommunications; Bandwidth (computing); Nanotechnology","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":[0.0002217982,0.0001762063,0.0001976452,0.0002132881,0.0001663869,0.0001027315,0.0004121,0.00009152525,0.00006140853],"category_scores_gemma":[0.00002404658,0.000149909,0.00003020643,0.0007237295,0.00006905603,0.0001633416,0.00002733255,0.0001038585,0.0001577559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002764459,"about_ca_system_score_gemma":0.0000365879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001500271,"about_ca_topic_score_gemma":0.000004885021,"domain_scores_codex":[0.9985147,0.000108913,0.0002092783,0.0004046548,0.0003094145,0.0004530526],"domain_scores_gemma":[0.9992586,0.00003079067,0.00009608097,0.0004988621,0.00005456719,0.00006111357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002356083,0.0000127201,0.00007059607,0.00001516053,0.00001572417,0.00004502774,0.0001209316,0.000001726355,0.9941888,0.00121246,0.000700419,0.003592799],"study_design_scores_gemma":[0.001272875,0.001695803,0.08000556,0.00007161645,0.00002865454,0.0003686412,0.00004085539,0.01435587,0.8873162,0.001002179,0.01321639,0.0006253682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882815,0.00001227509,0.007137135,0.001972901,0.0003394474,0.0003112907,0.000006298955,0.001137338,0.0008018243],"genre_scores_gemma":[0.9891978,0.00002288718,0.007741179,0.0001563302,0.0001440859,0.000008208265,0.00008764531,0.00002320984,0.002618646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1068727,"threshold_uncertainty_score":0.611311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169673788013231,"score_gpt":0.2142825915653106,"score_spread":0.1973152127639875,"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."}}