{"id":"W2925148167","doi":"10.1109/cvpr42600.2020.01164","title":"nuScenes: A Multimodal Dataset for Autonomous Driving","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":379,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"","keywords":"Computer science; Lidar; Artificial intelligence; Benchmark (surveying); Computer vision; Object detection; Segmentation; Bounding overwatch; Suite; Radar; Field (mathematics); Minimum bounding box; Software deployment; Tracking (education); Image (mathematics); Remote sensing; Geography","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":[0.000443896,0.002787403,0.00108769,0.002103729,0.001004906,0.001024411,0.002395593,0.001909621,0.008997487],"category_scores_gemma":[0.001357105,0.0004637236,0.001326945,0.002349565,0.0004841402,0.001009198,0.001495297,0.001355299,0.01176632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001069402,"about_ca_system_score_gemma":0.00120266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04226633,"about_ca_topic_score_gemma":0.0991421,"domain_scores_codex":[0.9991444,0.0000946691,0.00006092219,0.0002810584,0.00028709,0.0001318941],"domain_scores_gemma":[0.999494,0.00006085673,0.00004291564,0.0001711392,0.0001663323,0.00006482033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005675305,0.0004413146,0.006735447,0.001406583,0.0002441874,0.0003325309,0.0001752378,0.007380895,0.004364965,0.0008354713,0.9234756,0.05404011],"study_design_scores_gemma":[0.000433744,0.000540578,0.06071131,0.0005186535,0.0001807395,0.001236987,0.001038383,0.04999965,0.01338493,0.004292027,0.8673509,0.0003121547],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01932013,0.0006667894,0.002548195,0.0002610775,0.000198508,0.0002488823,0.9638898,0.00824934,0.004617165],"genre_scores_gemma":[0.01264282,0.0001009253,0.003342692,0.0000560085,0.00001770927,0.000137022,0.9825863,0.0001838218,0.0009328017],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04226633,"threshold_uncertainty_score":0.08404064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04817872216276899,"score_gpt":0.3202832048929103,"score_spread":0.2721044827301414,"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."}}