{"id":"W2892005580","doi":"10.1109/crv.2019.00023","title":"Mapless Online Detection of Dynamic Objects in 3D Lidar","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Computer science; Benchmark (surveying); Distortion (music); Computer vision; Artificial intelligence; Motion (physics); Independence (probability theory); Motion compensation; Compensation (psychology); Remote sensing; Geography; Geodesy; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003995718,0.0006736809,0.0008571866,0.000844002,0.0004562143,0.001016172,0.002326458,0.0008909705,0.001384077],"category_scores_gemma":[0.002012728,0.0006545621,0.0006392,0.0006806935,0.0005415378,0.00214473,0.002469124,0.0008717477,0.001000635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003798835,"about_ca_system_score_gemma":0.0006663926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001954976,"about_ca_topic_score_gemma":0.003693571,"domain_scores_codex":[0.999042,0.0001149303,0.00002844935,0.0002023126,0.0005198395,0.00009246431],"domain_scores_gemma":[0.9992195,0.0002027194,0.00008218727,0.0002828416,0.0001667917,0.0000458816],"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":[0.0003765793,0.0002761583,0.005233751,0.0002912761,0.0001274438,0.0003998422,0.0004450627,0.1430976,0.170957,0.009191017,0.005276734,0.6643275],"study_design_scores_gemma":[0.00001238125,0.00004390356,0.000903077,0.000008882489,0.000008418519,0.0001874733,0.00004385456,0.9673891,0.02619033,0.002977759,0.002213445,0.00002133519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03259982,0.0001385122,0.9635777,0.00005152104,0.00003078281,0.00002985855,0.0001282014,0.002846236,0.0005973095],"genre_scores_gemma":[0.4882431,0.0001736107,0.509122,0.0001251426,0.00005458696,0.0001122819,0.0005253646,0.0004073791,0.001236727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002326458,"threshold_uncertainty_score":0.004630208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008890056682483967,"score_gpt":0.2445002331010703,"score_spread":0.2356101764185863,"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."}}