{"id":"W2751512881","doi":"10.1371/journal.pone.0184564","title":"The impact of vehicle moving violations and freeway traffic flow on crash risk: An application of plugin development for microsimulation","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Traffic control and management","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Microsimulation; Plug-in; Crash; Collision; Transport engineering; Computer science; Cluster analysis; Traffic flow (computer networking); Traffic simulation; Engineering; Computer security; Machine learning","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":[0.0001268744,0.00006112197,0.00009658789,0.00002842614,0.0002067365,0.00002699834,0.00008988638,0.00002335746,0.000001166502],"category_scores_gemma":[0.00002456854,0.00004852068,0.0000245037,0.00001787865,0.00001728647,0.00006941657,0.000009742241,0.0000331745,6.697137e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002833281,"about_ca_system_score_gemma":0.000007340803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002183422,"about_ca_topic_score_gemma":0.0001622432,"domain_scores_codex":[0.9996147,0.000006529775,0.0001456667,0.00007736065,0.00007245276,0.00008329248],"domain_scores_gemma":[0.999568,0.00006849216,0.0000820261,0.0002221385,0.00003643041,0.00002295232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003646162,0.0002446909,0.004261247,0.00007806062,0.0002062006,2.185262e-8,0.00057644,0.7031103,0.07142695,0.00007135866,0.00001224192,0.2199761],"study_design_scores_gemma":[0.0003321808,0.00004228748,0.338805,0.0000221789,0.0000275936,9.331749e-9,0.000009181873,0.6568999,0.003771957,0.00003170815,0.00001871848,0.00003924777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881206,0.00004927339,0.01126306,0.00002038447,0.00001049576,0.0004396415,0.00001853804,0.00003881493,0.00003920094],"genre_scores_gemma":[0.9973353,0.00003051651,0.002525898,7.989055e-7,0.00002112073,0.00005656685,0.00001032482,0.00001017542,0.000009363861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3345438,"threshold_uncertainty_score":0.1978616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0191895517032812,"score_gpt":0.2337090913310072,"score_spread":0.214519539627726,"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."}}