{"id":"W2128052982","doi":"10.3141/2386-19","title":"Motion Prediction Methods for Surrogate Safety Analysis","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"National Research Council Canada; National Science Council; Kentucky Transportation Cabinet; World Health Organization","keywords":"Collision; Computer science; Acceleration; Code (set theory); Set (abstract data type); Sampling (signal processing); Poison control; Motion (physics); Work (physics); Simulation; Data mining; Engineering; Artificial intelligence; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005853856,0.0002495816,0.0005377604,0.001450257,0.0005911898,0.0001247807,0.0007375601,0.000246339,0.0005376771],"category_scores_gemma":[0.000133408,0.0001879022,0.0007728846,0.003337184,0.0003105314,0.0008192627,0.000003681455,0.001558486,0.00002378559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003025802,"about_ca_system_score_gemma":0.0001705963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002115059,"about_ca_topic_score_gemma":0.007162283,"domain_scores_codex":[0.9944726,0.00102879,0.001539059,0.0003335363,0.001777716,0.0008483308],"domain_scores_gemma":[0.99417,0.00132459,0.0002270986,0.0004401462,0.003463421,0.0003748086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00166007,0.0004326845,0.269792,0.0009895426,0.003939968,0.00001830245,0.00559873,0.4046682,0.01426279,0.003843663,0.01766397,0.2771301],"study_design_scores_gemma":[0.001271692,0.0002507287,0.9488214,0.00008544223,0.0003136496,2.427686e-7,0.0009434075,0.02701289,0.001527409,0.002053918,0.01753667,0.0001826019],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6086283,0.0003027153,0.3865981,0.001347648,0.0008850473,0.001684641,0.0002214701,0.0001159031,0.0002162094],"genre_scores_gemma":[0.9732691,0.001263002,0.02430355,0.00001549275,0.0002258902,0.0002139248,0.0001013402,0.00007090315,0.000536783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6790294,"threshold_uncertainty_score":0.766243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06403416361299222,"score_gpt":0.3882630882571519,"score_spread":0.3242289246441598,"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."}}