{"id":"W6976590211","doi":"10.60692/2p9dw-1xh07","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105682,0.0009556136,0.0005151145,0.00113593,0.000266041,0.0007499969,0.0009291724,0.0009040168,0.003076442],"category_scores_gemma":[0.00249698,0.0003165181,0.0005817162,0.0009790326,0.0002639309,0.001126575,0.0006757746,0.00114319,0.001514992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009037456,"about_ca_system_score_gemma":0.0007852595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009808607,"about_ca_topic_score_gemma":0.01071708,"domain_scores_codex":[0.9994562,0.00009673175,0.00003553225,0.000159843,0.0001661474,0.00008543795],"domain_scores_gemma":[0.9990751,0.0002618346,0.0000905023,0.0001414963,0.000392078,0.00003903864],"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":[0.0004258201,0.0003694615,0.007191736,0.0002012399,0.0001917298,0.0001491923,0.00006290078,0.2502342,0.02384047,0.002904612,0.01126993,0.7031586],"study_design_scores_gemma":[0.000005976872,0.00004914367,0.001160378,0.00001260204,0.00001217385,0.00001918062,0.00001097322,0.988322,0.008249901,0.001049957,0.001101078,0.000006610755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2409762,0.002901278,0.7335577,0.001248518,0.000405845,0.0001778616,0.001814121,0.009116978,0.009801571],"genre_scores_gemma":[0.825698,0.0005405018,0.1633346,0.0003720541,0.00009671483,0.00009408269,0.00305648,0.0001345141,0.006673178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009808607,"threshold_uncertainty_score":0.019503,"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."}}