{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001355251,0.0005377936,0.0003205133,0.001201459,0.0003004379,0.0006172127,0.00131809,0.001195194,0.001748244],"category_scores_gemma":[0.004946186,0.0001782451,0.0007225613,0.001395626,0.0004006746,0.000556789,0.0006120157,0.001007869,0.000623484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007928594,"about_ca_system_score_gemma":0.0006933484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005465308,"about_ca_topic_score_gemma":0.006437319,"domain_scores_codex":[0.999161,0.0002602241,0.00006136016,0.000193543,0.0002351915,0.0000886927],"domain_scores_gemma":[0.9973742,0.001138258,0.0001636226,0.0005430477,0.0006398799,0.0001410378],"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":[0.001038361,0.002324332,0.06976321,0.0007728501,0.000328692,0.001619889,0.0004104588,0.6938128,0.009738285,0.01182528,0.1195861,0.08877978],"study_design_scores_gemma":[0.0002409584,0.0006528176,0.06216642,0.0001214363,0.00006330775,0.0007259408,0.0006363792,0.8435108,0.01507953,0.008387215,0.06830114,0.00011407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7115101,0.0005415565,0.07443581,0.001446788,0.0006531135,0.001123497,0.198489,0.003714758,0.00808531],"genre_scores_gemma":[0.6857872,0.0002769075,0.05575726,0.0002992913,0.00008416007,0.001021172,0.2540797,0.0001555418,0.002538741],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.005465308,"threshold_uncertainty_score":0.010867,"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."}}