{"id":"W1495166530","doi":"10.1016/j.aap.2015.07.014","title":"Mapping cyclist activity and injury risk in a network combining smartphone GPS data and bicycle counts","year":2015,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Police Service; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Global Positioning System; Geocoding; Intersection (aeronautics); Computer science; Transport engineering; Poison control; Data collection; Geography; Engineering; Cartography; Telecommunications; Statistics","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":[0.0005818325,0.00008606218,0.0001854956,0.0001371405,0.00004618611,0.00004013111,0.00008995994,0.00005274433,0.00002089073],"category_scores_gemma":[0.00002159474,0.00008930529,0.00003132374,0.0004477275,0.00001433649,0.000266773,0.000112722,0.0001185539,0.000009910646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003333036,"about_ca_system_score_gemma":0.000007886255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001686624,"about_ca_topic_score_gemma":0.002251272,"domain_scores_codex":[0.9993582,0.00005443714,0.0001679086,0.0001844964,0.0001051451,0.0001297516],"domain_scores_gemma":[0.9996091,0.0000264426,0.00005615775,0.000242564,0.00001128091,0.00005443126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001078178,0.00001815898,0.9474257,0.000003275762,0.0002739117,0.000001643834,0.000156794,0.007302092,0.00002073981,0.000008527334,0.001026879,0.04375155],"study_design_scores_gemma":[0.0002384391,0.000006677408,0.849418,0.00002311186,0.0002109767,7.505162e-7,0.00006777072,0.1491714,0.000004865377,0.0002804286,0.0004802636,0.00009733049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800912,0.0008769654,0.01832109,0.00001446759,0.000103812,0.00006791486,0.000001970259,0.00006233323,0.0004602323],"genre_scores_gemma":[0.9988285,0.0005273185,0.0004435913,0.000003961137,0.00005349784,0.000003362561,0.00007833799,0.000008854309,0.00005262922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1418693,"threshold_uncertainty_score":0.3641764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02254595757177751,"score_gpt":0.2652813447635994,"score_spread":0.2427353871918219,"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."}}