{"id":"W3211837290","doi":"10.1109/mfi52462.2021.9591167","title":"Detection of Conductive Lane Markers using mm Wave FMCW Automotive Radar","year":2021,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Radar; Electrical conductor; Snow; Automotive industry; Computer science; Extremely high frequency; Remote sensing; Radar imaging; Lidar; Automotive engineering; Aerospace engineering; Telecommunications; Geology; Engineering; Electrical engineering; Meteorology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000436394,0.0000603598,0.0001064427,0.00001941309,0.00002411995,0.000005591675,0.00002220836,0.0000332157,0.00009272161],"category_scores_gemma":[0.00002946489,0.00005875263,0.00004053319,0.0001903294,0.00002058931,0.00004158611,0.00001336566,0.00007151837,0.00000700268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002765382,"about_ca_system_score_gemma":0.000009563018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000406145,"about_ca_topic_score_gemma":0.000007043351,"domain_scores_codex":[0.9996451,0.00002462256,0.00009544237,0.00009413352,0.00005524535,0.00008549872],"domain_scores_gemma":[0.9997072,0.00006264191,0.0000161703,0.0001100105,0.00007443144,0.00002951893],"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":[0.000001740894,0.00001622253,0.000004389989,0.00001882318,0.00003901177,0.000001449828,0.00006569384,0.0006980763,0.985087,0.001070169,0.0000184967,0.01297892],"study_design_scores_gemma":[0.0001316419,0.00001578539,0.003633838,0.00001146749,0.00002706751,0.000007149463,0.0005316366,0.04000327,0.9520444,0.003002997,0.0004865411,0.00010418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7373477,0.00004219647,0.2534538,0.00004157721,0.0001256951,0.00008701401,0.00001078595,0.000108147,0.008783109],"genre_scores_gemma":[0.8837419,0.000004667409,0.1160369,0.00001077524,0.0000276729,0.000004520406,0.000002562317,0.00001004048,0.00016094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1463943,"threshold_uncertainty_score":0.2395863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02828805979396306,"score_gpt":0.2579381522518057,"score_spread":0.2296500924578427,"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."}}