{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003010271,0.0003358599,0.0002162886,0.000603758,0.0001127307,0.0004477632,0.0004452443,0.0006100694,0.0005805223],"category_scores_gemma":[0.0008273417,0.0001476956,0.0001294398,0.0002562895,0.000201525,0.0007106494,0.0004917253,0.0003037143,0.0004908941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110081,"about_ca_system_score_gemma":0.0001238579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002047486,"about_ca_topic_score_gemma":0.0003058801,"domain_scores_codex":[0.9997047,0.00006338505,0.00001022603,0.00005310474,0.0001243964,0.00004414107],"domain_scores_gemma":[0.9994978,0.000158728,0.0001111936,0.00005130919,0.0001534815,0.00002749276],"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":[0.0002226866,0.0001206479,0.00612518,0.0001275659,0.00002713367,0.0002159195,0.0002602708,0.004323273,0.8280599,0.0008664417,0.0005970006,0.1590541],"study_design_scores_gemma":[0.00006009136,0.001419594,0.0244066,0.00005835335,0.00006749472,0.001423954,0.0005090971,0.1337072,0.8295109,0.001392572,0.007376238,0.00006791998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5934104,0.0004610244,0.4010855,0.0001578756,0.000096247,0.00004421372,0.00006908803,0.0008980379,0.003777678],"genre_scores_gemma":[0.9319043,0.0001900803,0.06609224,0.00008423653,0.00003039084,0.00002083448,0.00006490012,0.00002110131,0.001591799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006100694,"threshold_uncertainty_score":0.001942039,"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."}}