{"id":"W6939231492","doi":"10.60692/b9w37-vh070","title":"DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Object detection; Lidar; Deep learning; Adverse weather; Radar; Object (grammar); Ranging; Radar imaging","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":[0.0003828804,0.0001191778,0.0001278381,0.0002351223,0.0004372087,0.0002363248,0.0007040452,0.00005689746,6.629772e-7],"category_scores_gemma":[0.00003784381,0.0001128255,0.00003739365,0.0008840844,0.00001178685,0.002287182,0.0003429335,0.00008713196,0.0003413999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008622409,"about_ca_system_score_gemma":0.00001901691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002212174,"about_ca_topic_score_gemma":2.647546e-7,"domain_scores_codex":[0.9988647,0.00004131175,0.0003816364,0.000243126,0.0002048972,0.0002643187],"domain_scores_gemma":[0.9987241,0.00003044693,0.000260961,0.000819949,0.000108243,0.00005632275],"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":[0.0001024964,0.000005437377,0.01211893,0.001269896,0.0001609886,0.000007818869,0.0866587,0.6439645,0.0004342007,0.006217693,0.0004091151,0.2486502],"study_design_scores_gemma":[0.0002616819,0.00001599427,0.001578493,0.00002326831,0.000007222581,0.00002556006,0.001049162,0.9949212,0.0004796941,0.0000119867,0.001495886,0.0001298543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02948569,0.000002002231,0.9684291,0.0000298441,0.000353801,0.0004841426,0.00001335779,0.001079401,0.0001226184],"genre_scores_gemma":[0.973977,1.778897e-7,0.02568476,0.00004155347,0.0001034789,0.00008705533,0.0000581794,0.000009670017,0.00003806686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9444914,"threshold_uncertainty_score":0.4600892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08728564726637203,"score_gpt":0.2737627561174449,"score_spread":0.1864771088510729,"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."}}