{"id":"W2798873012","doi":"10.1109/cvpr.2018.00615","title":"LiDAR-Video Driving Dataset: Learning Driving Policies Effectively","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lidar; Computer science; Dashboard; Point cloud; Scale (ratio); Computer vision; Artificial intelligence; Remote sensing; Data science","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":[0.000212136,0.00016257,0.0001434946,0.00009800809,0.0005928242,0.000202607,0.0009938509,0.00004250242,0.00003959883],"category_scores_gemma":[0.0001607349,0.0001488043,0.00003711363,0.000667738,0.0001301872,0.0008589382,0.0008493894,0.0002207402,0.0005203068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004742367,"about_ca_system_score_gemma":0.00002322879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004014123,"about_ca_topic_score_gemma":0.00008162994,"domain_scores_codex":[0.9985971,0.00008649736,0.0001890291,0.0005023428,0.0002018914,0.0004231296],"domain_scores_gemma":[0.9985324,0.0004616553,0.0001002088,0.0007123748,0.00007274204,0.0001206868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008671643,0.0001946035,0.06855866,0.00002705352,0.00009007924,0.00002049648,0.003019102,0.003356977,0.08687675,0.4270215,0.05316161,0.3576645],"study_design_scores_gemma":[0.0006175621,0.000526763,0.1118477,0.0001260127,0.00002106403,0.0001011627,0.0001276916,0.2309161,0.07388614,0.01492539,0.5656001,0.001304339],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03885428,0.0000204298,0.9530012,0.001100346,0.0001430229,0.0001915995,0.000002612162,0.000621049,0.006065466],"genre_scores_gemma":[0.9299572,0.000008854356,0.06850608,0.0005890782,0.0003250291,0.00001527391,0.00001417999,0.00001665172,0.0005676806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8911029,"threshold_uncertainty_score":0.6687664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586056699650176,"score_gpt":0.291744235751821,"score_spread":0.2758836687553192,"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."}}