{"id":"W2601645953","doi":"10.15353/vsnl.v2i1.101","title":"Spatial Detection of Vehicles in Images using Convolutional Neural Networks and Stereo Matching","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Matching (statistics); Computer science; Pixel; Computer vision; Pattern recognition (psychology); Artificial neural network; RADIUS; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002949301,0.00007928957,0.0001870215,0.0001910643,0.00004326935,0.00002097376,0.00004201452,0.00004246154,0.000001205146],"category_scores_gemma":[0.00001119542,0.00005836159,0.00002667833,0.0000576391,0.00007426747,0.0002686436,0.00001960778,0.0001216849,1.418038e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004375377,"about_ca_system_score_gemma":0.00001256738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002240167,"about_ca_topic_score_gemma":0.000001654216,"domain_scores_codex":[0.9992633,0.00004649707,0.0004215748,0.0000586825,0.0001198004,0.00009007282],"domain_scores_gemma":[0.9995359,0.0001559774,0.0001649581,0.00002998458,0.00008166488,0.00003151545],"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":[0.00006718575,0.00001957072,0.03730534,0.0000776133,0.00003748376,0.00001374422,0.0001413684,0.8164802,0.0453981,0.0003267823,0.00001397121,0.1001186],"study_design_scores_gemma":[0.0006070866,0.00003075216,0.1180314,0.0002379336,0.000006970773,0.000394698,0.00007677079,0.8793692,0.0002440343,0.000925219,0.00001229111,0.00006364445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6015267,0.001091299,0.3971009,0.00006269872,0.0001715114,0.00002837125,0.00000185241,0.00001230184,0.000004322437],"genre_scores_gemma":[0.999288,0.0000391531,0.0005878465,0.000007363657,0.00006719343,3.495686e-7,3.531369e-7,0.000008106987,0.000001589212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3977613,"threshold_uncertainty_score":0.2379916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00561190050870166,"score_gpt":0.2232672966516188,"score_spread":0.2176553961429172,"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."}}