{"id":"W2596423617","doi":"10.3141/2659-05","title":"Impact of Congestion and Traffic Flow on Crash Frequency and Severity: Application of Smartphone-Collected GPS Travel Data","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Global Positioning System; Crash; Computer science; Traffic congestion; Transport engineering; Geography; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007071286,0.0001617741,0.0004677419,0.0006621204,0.001582945,0.0001470409,0.00121622,0.000191918,0.0001399367],"category_scores_gemma":[0.0009481483,0.0001287045,0.0002020401,0.0008960973,0.002124621,0.0007299883,0.000009985622,0.0009258336,0.000001759645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001606994,"about_ca_system_score_gemma":0.001198939,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09684702,"about_ca_topic_score_gemma":0.3623097,"domain_scores_codex":[0.9945435,0.001294862,0.001021849,0.000400146,0.002318447,0.0004211878],"domain_scores_gemma":[0.9940041,0.001126035,0.0007989343,0.0008607158,0.002912641,0.0002975711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001324163,0.0005882983,0.8892926,0.0003261143,0.0003514452,0.00001094936,0.01378824,0.001435484,0.003568937,0.00186373,0.000544131,0.08690591],"study_design_scores_gemma":[0.001001208,0.0005010681,0.9917544,0.0001945687,0.00008701906,1.281262e-7,0.002031985,0.002097568,0.0001655176,0.001913895,0.0001436138,0.0001090352],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957005,0.0001046133,0.001002716,0.001909114,0.00007566554,0.0007994854,0.0002587595,0.000008252395,0.0001408716],"genre_scores_gemma":[0.997713,0.001421293,0.0005591919,0.000006217163,0.0000837972,0.00002380844,0.00005652414,0.0000177711,0.0001184548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2654627,"threshold_uncertainty_score":0.9997169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1202826577772915,"score_gpt":0.440600877332442,"score_spread":0.3203182195551505,"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."}}