{"id":"W2037982005","doi":"10.3141/2434-13","title":"Use of Drivers’ Jerk Profiles in Computer Vision–Based Traffic Safety Evaluations","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Qatar National Research Fund; Qatar Foundation","keywords":"Jerk; Acceleration; Action (physics); Computer science; Identification (biology); sort; Simulation; Control theory (sociology); Artificial intelligence; Control (management); Information retrieval","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":[0.003321214,0.0006987586,0.0004982349,0.004715056,0.0002643336,0.001323503,0.0003873072,0.0004891875,0.0007615958],"category_scores_gemma":[0.01303977,0.0001922314,0.0003002537,0.001846388,0.0003117812,0.00129707,0.0006646801,0.0004695146,0.0004353863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004326265,"about_ca_system_score_gemma":0.0002958869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002031692,"about_ca_topic_score_gemma":0.002580979,"domain_scores_codex":[0.9975872,0.001133389,0.0002087851,0.000293052,0.0006597802,0.000117759],"domain_scores_gemma":[0.994802,0.002436642,0.0008750112,0.0002370898,0.001360234,0.0002890846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004320007,0.001273612,0.3003231,0.0006788577,0.0004562639,0.0004122739,0.001911409,0.03650869,0.03698077,0.002127008,0.002409611,0.6125984],"study_design_scores_gemma":[0.00012202,0.002234027,0.7613639,0.0002183728,0.0002277315,0.000630435,0.002270297,0.2080868,0.01796293,0.00338128,0.003256053,0.0002461197],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463504,0.0004590146,0.04432426,0.00007838586,0.00007763572,0.0003204265,0.0006054888,0.0004073287,0.007377021],"genre_scores_gemma":[0.988188,0.0001404357,0.01090653,0.00002218182,0.00001494019,0.00006357922,0.000321941,0.00001533325,0.000327159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004715056,"threshold_uncertainty_score":0.01756448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07189191753717128,"score_gpt":0.3549138646936942,"score_spread":0.2830219471565229,"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."}}