{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001741962,0.0004986832,0.0004812211,0.0003784336,0.0001291858,0.00007246446,0.0005261498,0.0003998836,0.00004503493],"category_scores_gemma":[0.00005716378,0.0006855226,0.0004039383,0.0004114589,0.00008345537,0.0002659597,0.0002332517,0.0007202611,0.00001165564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008813285,"about_ca_system_score_gemma":0.000100443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009936716,"about_ca_topic_score_gemma":0.0002648208,"domain_scores_codex":[0.9982544,0.00007268553,0.0002410843,0.0008763415,0.000089121,0.0004663768],"domain_scores_gemma":[0.9985098,0.0001076694,0.0001247568,0.0009809349,0.0001516007,0.0001252016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009702078,0.00004183236,0.0002474431,0.0004960019,0.0002079538,0.0001577529,0.00008946312,0.9876044,0.006927548,0.00008368017,0.0002558745,0.003791001],"study_design_scores_gemma":[0.0009398877,0.00003554882,0.0004444006,0.0001999561,0.000206533,0.000006559327,0.0001111226,0.9791551,0.01506182,0.002095882,0.0009072908,0.0008359133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1677093,0.0001965334,0.8283765,0.00001824224,0.0007011088,0.0007096629,0.00008515195,0.001899688,0.000303733],"genre_scores_gemma":[0.9806464,0.00020752,0.0184657,0.00004251235,0.00006724623,0.00001486073,0.0001543816,0.00015599,0.0002454111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.812937,"threshold_uncertainty_score":0.9995596,"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."}}