{"id":"W3158167351","doi":"10.48550/arxiv.2105.00363","title":"RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Minimum bounding box; Computer science; Azimuth; Bounding overwatch; Artificial intelligence; Computer vision; Radar; Deep learning; Range (aeronautics); Doppler effect; Doppler radar; Object detection; Remote sensing; Pattern recognition (psychology); Geography; Image (mathematics); Mathematics; Telecommunications; Engineering; Physics","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":[0.0009538038,0.00305902,0.001285097,0.002315743,0.0004622091,0.001309971,0.002741978,0.001612356,0.004698758],"category_scores_gemma":[0.002572818,0.0006134181,0.001045296,0.001640847,0.0004120358,0.001624204,0.001691976,0.001764627,0.009012666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007549018,"about_ca_system_score_gemma":0.00100512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008199271,"about_ca_topic_score_gemma":0.02066965,"domain_scores_codex":[0.9987368,0.0001481416,0.00006312586,0.0004936802,0.0003563556,0.000201955],"domain_scores_gemma":[0.9990705,0.0001545102,0.00009641338,0.0003846544,0.0002183178,0.0000756525],"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":[0.001350635,0.001003434,0.02062989,0.001329186,0.0004884648,0.0004330522,0.0001232324,0.03681643,0.02549153,0.002249601,0.3206633,0.5894212],"study_design_scores_gemma":[0.0002521658,0.0007155708,0.02762737,0.0002667663,0.0001931771,0.001668395,0.000293228,0.7594433,0.0815282,0.006514094,0.1213165,0.0001811881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2768933,0.005977205,0.3121389,0.001309261,0.001718548,0.001270681,0.2270948,0.1519163,0.02168106],"genre_scores_gemma":[0.2627616,0.001075844,0.276244,0.0006007436,0.0001878191,0.0004939912,0.4459062,0.001279057,0.01145065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008199271,"threshold_uncertainty_score":0.01630312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03083245339002934,"score_gpt":0.1884772888257273,"score_spread":0.1576448354356979,"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."}}