{"id":"W3119833346","doi":"10.3390/s21020461","title":"A Multilane Tracking Algorithm Using IPDA with Intensity Feature","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Dynamics (Canada); McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; False positive paradox; Frame (networking); Filter (signal processing); Feature (linguistics); Set (abstract data type); Pixel; Pattern recognition (psychology); Tracking (education); Probabilistic logic; Computer vision","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":[0.00004428131,0.0001330354,0.0001814188,0.00004221153,0.0000767383,0.00001359029,0.0000595351,0.0001775533,0.0000218209],"category_scores_gemma":[0.00001270717,0.0001235416,0.00003667156,0.0001888275,0.00005199436,0.0000483771,0.00002276078,0.0003872318,0.00001611837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000561469,"about_ca_system_score_gemma":0.0000192547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008850102,"about_ca_topic_score_gemma":0.00003543854,"domain_scores_codex":[0.9994232,0.00001196773,0.00008831565,0.0001705793,0.00007206674,0.0002338495],"domain_scores_gemma":[0.9996467,0.00001760618,0.00001719199,0.0002123046,0.0000652424,0.00004094099],"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":[0.00008042563,0.0001195748,0.008564581,0.00017039,0.0007028059,0.005980905,0.002706868,0.2505017,0.04891432,0.0002764806,0.0006947782,0.6812872],"study_design_scores_gemma":[0.0005305213,0.00002585544,0.009093825,0.00007657188,0.0000562457,0.001293848,0.0008561109,0.8551431,0.1288014,0.00007958943,0.003619174,0.0004237695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926798,0.0002311942,0.005501678,0.000152823,0.0001522777,0.00005557507,0.000008482364,0.000693512,0.0005246222],"genre_scores_gemma":[0.9678811,0.00002288143,0.03174653,0.00005532672,0.00005239369,6.333383e-7,0.000008105383,0.00003055728,0.0002024211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6808634,"threshold_uncertainty_score":0.5037881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009998182942996715,"score_gpt":0.2052741510493831,"score_spread":0.1952759681063864,"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."}}