{"id":"W2990710640","doi":"10.1016/j.physa.2019.123534","title":"Allometric scaling of road accidents using social media crowd-sourced data","year":2019,"lang":"en","type":"article","venue":"Physica A Statistical Mechanics and its Applications","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Conseil National de la Recherche Scientifique","keywords":"Crash; Social media; Transport engineering; Population; Intervention (counseling); Cluster analysis; Data collection; Computer security; Computer science; Engineering; Geography; Statistics; Psychology; Mathematics; Demography; Sociology; World Wide Web","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":[0.001853927,0.000702645,0.0005150713,0.005534301,0.0005336618,0.001299405,0.000708867,0.0005881237,0.001408794],"category_scores_gemma":[0.0123561,0.0002402582,0.0007454467,0.004977757,0.0004622928,0.001695526,0.001273992,0.0004970005,0.0008029335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005170905,"about_ca_system_score_gemma":0.0004302818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006545792,"about_ca_topic_score_gemma":0.005530505,"domain_scores_codex":[0.9986058,0.0004921472,0.0001012678,0.0003606413,0.0003438278,0.00009639658],"domain_scores_gemma":[0.9957558,0.001680411,0.0007526045,0.0007945896,0.0008234935,0.0001931669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007815808,0.000727717,0.4543793,0.0004896435,0.001147109,0.0006620532,0.002304466,0.171482,0.009573204,0.01029073,0.01095862,0.3372036],"study_design_scores_gemma":[0.00001298847,0.0001242309,0.298423,0.00004798846,0.00007305916,0.0003265836,0.001057645,0.6847649,0.001557071,0.01032989,0.003198105,0.00008462073],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9146557,0.0005964286,0.07635638,0.0002912472,0.0001923563,0.00008542153,0.00409438,0.0007094574,0.003018789],"genre_scores_gemma":[0.9836638,0.0001647402,0.0120356,0.00001630538,0.0001200979,0.00005486223,0.003433464,0.00005934364,0.0004517876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006545792,"threshold_uncertainty_score":0.01301539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07268378012802307,"score_gpt":0.3642830681260062,"score_spread":0.2915992879979831,"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."}}