{"id":"W4240606069","doi":"10.32920/ryerson.14652873","title":"A real-time pedestrian detection system for safety applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pedestrian; Brake; Pedestrian detection; Computer science; Warning system; Real-time computing; Advanced driver assistance systems; Artificial intelligence; Computer vision; Engineering; Automotive engineering; Transport engineering; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004622067,0.0005594419,0.0005095027,0.0007137851,0.0003833669,0.000479768,0.0006957136,0.0008602033,0.005738337],"category_scores_gemma":[0.0005308254,0.0003093884,0.000262926,0.0004585439,0.0001327687,0.0005614252,0.0003563983,0.0004398962,0.00322368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002980241,"about_ca_system_score_gemma":0.0004690608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001491722,"about_ca_topic_score_gemma":0.001305343,"domain_scores_codex":[0.9997271,0.00003875698,0.00001644053,0.00007289855,0.0001135257,0.00003126913],"domain_scores_gemma":[0.9996665,0.00003078826,0.00002096142,0.00004564575,0.0001943033,0.00004195037],"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":[0.001425082,0.0004339805,0.00338187,0.0003369448,0.00007235394,0.0004784487,0.0002569055,0.006691838,0.4619344,0.002103392,0.02630856,0.4965763],"study_design_scores_gemma":[0.0002208202,0.002039065,0.01867741,0.00007647504,0.0001951742,0.002040974,0.0001198337,0.5565107,0.34708,0.001293015,0.07157478,0.0001718196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1038912,0.00102616,0.8542826,0.0002734449,0.0004934017,0.0004696105,0.0008366912,0.03189835,0.006828493],"genre_scores_gemma":[0.6475636,0.0004688003,0.3310949,0.000268152,0.0001797654,0.0003235512,0.00152911,0.0002434139,0.01832874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005738337,"threshold_uncertainty_score":0.01919669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02698125875405935,"score_gpt":0.3011514071514976,"score_spread":0.2741701483974383,"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."}}