{"id":"W3126503003","doi":"10.3390/risks9040058","title":"Synthetic Dataset Generation of Driver Telematics","year":2021,"lang":"en","type":"preprint","venue":"Risks","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Society of Actuaries","keywords":"Telematics; Computer science; Machine learning; Data mining; Artificial intelligence; Automatic summarization; Artificial neural network; Set (abstract data type); Feature (linguistics)","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":[0.00008693861,0.0001126415,0.0001696949,0.00007737891,0.00001191917,0.00002932387,0.0001356262,0.0001307989,0.00005570526],"category_scores_gemma":[0.00001228075,0.000122553,0.00004694898,0.00004011896,0.00001551186,0.00003577548,0.0001628466,0.000192896,0.000006896904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003195671,"about_ca_system_score_gemma":0.00001104695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001937407,"about_ca_topic_score_gemma":0.00001318223,"domain_scores_codex":[0.9994358,0.00001821917,0.0002153347,0.0001361159,0.0001189629,0.0000755839],"domain_scores_gemma":[0.9994557,0.000008894165,0.00004688736,0.0004434373,0.00002200799,0.00002301264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001102443,0.00007263198,0.00005771284,0.001744011,0.0002823078,0.00001404592,0.0003384627,0.1934,0.005350869,0.0006597982,0.7606865,0.03739253],"study_design_scores_gemma":[0.0001285157,0.00001321345,0.0007071179,0.0003310258,0.0002041987,0.000002974562,0.00006757257,0.9448077,0.02552567,0.00009188242,0.02777844,0.0003417065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09910914,0.0008093714,0.8892401,0.00004706527,0.001625947,0.0005468504,0.001879434,0.003347022,0.003395065],"genre_scores_gemma":[0.9863372,0.001125757,0.009236091,0.00001618343,0.00007498856,0.00003097278,0.003146028,0.00001992918,0.00001281508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8872281,"threshold_uncertainty_score":0.4997565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06514654319048209,"score_gpt":0.2868638642503256,"score_spread":0.2217173210598435,"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."}}