{"id":"W3208418061","doi":"10.1155/2021/9907698","title":"An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Oceans and Fisheries","keywords":"Truck; Yard; Collision; Port (circuit theory); Computer science; Metric (unit); Algorithm; Simulation; Real-time computing; Engineering; Automotive engineering; Computer security; Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007923843,0.001612742,0.001317866,0.002610197,0.001133836,0.001143523,0.002230748,0.001262179,0.001462191],"category_scores_gemma":[0.003473646,0.0004923567,0.001210562,0.001257666,0.0005065131,0.001406403,0.001559336,0.0009572816,0.001009436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008251191,"about_ca_system_score_gemma":0.001794391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00867011,"about_ca_topic_score_gemma":0.005048003,"domain_scores_codex":[0.9986235,0.0001234448,0.0001223891,0.0004494283,0.0005399761,0.0001413071],"domain_scores_gemma":[0.9987563,0.000329397,0.000181962,0.0000825522,0.0005810603,0.00006877448],"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":[0.0004937113,0.000346544,0.02546629,0.0001796491,0.0002256645,0.0006027545,0.0003167868,0.1840228,0.01427837,0.004421269,0.00432372,0.7653226],"study_design_scores_gemma":[0.00004020497,0.0001766323,0.004190072,0.00002478499,0.00006729388,0.000613776,0.0001359816,0.9824268,0.007311578,0.002849308,0.002123151,0.00004041587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03139443,0.0002204803,0.9652746,0.00008518062,0.0000788121,0.0002076494,0.0001094675,0.00163169,0.0009977007],"genre_scores_gemma":[0.3590777,0.0003315778,0.6356643,0.0001343127,0.00006673575,0.0005078327,0.0008406334,0.0001065366,0.003270325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00867011,"threshold_uncertainty_score":0.01723933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007017002396533412,"score_gpt":0.2237187964416112,"score_spread":0.2167017940450778,"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."}}