{"id":"W4366850722","doi":"10.48550/arxiv.2304.10591","title":"Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telematics; Computer science; Exploratory data analysis; Data science; Data mining; Econometrics; Telecommunications; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001525898,0.0004671318,0.0005234051,0.002518248,0.000261357,0.0009770887,0.001033406,0.00102972,0.001274068],"category_scores_gemma":[0.008795373,0.0001998103,0.0006071525,0.002900627,0.0003600791,0.0009371999,0.0007019138,0.0009689027,0.0006012704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004907481,"about_ca_system_score_gemma":0.0004456743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005047634,"about_ca_topic_score_gemma":0.005965176,"domain_scores_codex":[0.9988813,0.0003932886,0.0001201358,0.0003156262,0.0002109441,0.00007866355],"domain_scores_gemma":[0.9928114,0.003432725,0.001658286,0.001353256,0.0005452037,0.0001991041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007637924,0.0007457521,0.7549697,0.0005175833,0.0004578766,0.0009939397,0.0006930438,0.06275716,0.003633372,0.006505014,0.01906286,0.1489],"study_design_scores_gemma":[0.00007071908,0.0002615979,0.6255687,0.0002130456,0.0001267309,0.0008702177,0.001039441,0.3191853,0.005177105,0.02146538,0.02593653,0.00008530553],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8365843,0.001241429,0.1007215,0.002995681,0.00017417,0.0001749379,0.05392622,0.0006906408,0.003491157],"genre_scores_gemma":[0.9334825,0.000368113,0.02747597,0.0002176972,0.0001357295,0.000128658,0.03725466,0.00003465344,0.0009019489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005047634,"threshold_uncertainty_score":0.01003647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2734790201269569,"score_gpt":0.2266962976119477,"score_spread":0.0467827225150092,"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."}}