{"id":"W427383675","doi":"","title":"Use of Data Mining Technology to Investigate Vehicle Speed in Winter Weather: a Case Study","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snow; Measure (data warehouse); Computer science; Database; Association rule learning; Transport engineering; Data mining; Environmental science; Meteorology; Engineering; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001032894,0.0003720204,0.0002722963,0.00222399,0.0009379301,0.0009008809,0.001097767,0.0006740176,0.000446565],"category_scores_gemma":[0.002130444,0.0002394584,0.000394113,0.003594825,0.0004426413,0.0004105979,0.0003552599,0.0003252876,0.00008255768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002747183,"about_ca_system_score_gemma":0.002064662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.228854,"about_ca_topic_score_gemma":0.3116469,"domain_scores_codex":[0.9992002,0.000171628,0.00007441757,0.0001066685,0.0003437111,0.0001033911],"domain_scores_gemma":[0.9983721,0.0008507428,0.0001327646,0.0001184873,0.0004167013,0.0001091075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000594657,0.00159744,0.7874832,0.0002829479,0.0002267466,0.01704349,0.005057267,0.04752524,0.01340978,0.001651038,0.002208653,0.1229195],"study_design_scores_gemma":[0.0001139399,0.0008774123,0.7093745,0.00009339483,0.0002764112,0.004864331,0.02051483,0.2273807,0.02494354,0.001226134,0.01022723,0.0001076083],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99429,0.0001108123,0.002984168,0.0001441759,0.000005015168,0.00009909084,0.0005172201,0.000064379,0.001785239],"genre_scores_gemma":[0.9888201,0.000196425,0.009601176,0.00002277863,0.000003606636,0.00003423899,0.0005945726,0.000007288455,0.0007197122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.228854,"threshold_uncertainty_score":0.4550439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1613132548555975,"score_gpt":0.4066087095404349,"score_spread":0.2452954546848374,"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."}}