{"id":"W4388507408","doi":"10.1145/3628357.3629709","title":"MARS: a mmWave Rotating Synthetic Aperture Radar System for Indoor Imaging","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Synthetic aperture radar; Mars Exploration Program; Computer science; Inverse synthetic aperture radar; Radar imaging; Side looking airborne radar; Radar; Remote sensing; Acceleration; Back projection; Extremely high frequency; Computer vision; Artificial intelligence; Radar engineering details; Geology; Physics; Telecommunications","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":[0.0002010536,0.0001854318,0.0001970324,0.0001565039,0.00007906477,0.00005088411,0.0001745296,0.00006261271,0.000008823547],"category_scores_gemma":[0.0001451632,0.0001779235,0.00007591579,0.0002706384,0.0000201011,0.0001699738,0.00005139913,0.0001894311,0.00007324424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048428,"about_ca_system_score_gemma":0.000008454589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005148217,"about_ca_topic_score_gemma":0.000001422309,"domain_scores_codex":[0.9990263,0.00001291232,0.0002235021,0.0002214056,0.0001252085,0.0003906593],"domain_scores_gemma":[0.999288,0.0003163251,0.00002724372,0.0002764774,0.00003812928,0.00005387437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004812957,0.00003750898,0.001825562,0.006427523,0.0002802937,0.0003034542,0.002351051,0.008631814,0.3649493,0.01646357,0.09951027,0.4991716],"study_design_scores_gemma":[0.0003765514,0.00001386603,0.00005000259,0.0005014773,0.00002313168,0.00006986163,0.0009974223,0.9259923,0.04329204,0.001475887,0.02670122,0.0005062829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00371002,0.0002606956,0.9694879,0.0004781438,0.0003527957,0.0008493721,0.00004066293,0.01717138,0.007648994],"genre_scores_gemma":[0.7416089,0.00001043203,0.2573403,0.0001313686,0.0001026166,0.0003120692,0.00002385833,0.0001533803,0.0003170437],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9173604,"threshold_uncertainty_score":0.7255509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008849525601823897,"score_gpt":0.2361528625443887,"score_spread":0.2273033369425648,"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."}}