{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005139565,0.0003709935,0.0005907886,0.002882246,0.000202664,0.0001211917,0.0009882885,0.0002756643,0.00002982069],"category_scores_gemma":[0.0007379159,0.0003845045,0.00006622923,0.00424556,0.0004415716,0.001345082,0.00005817517,0.001509628,0.00003036742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002504663,"about_ca_system_score_gemma":0.00037729,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01486925,"about_ca_topic_score_gemma":0.05123656,"domain_scores_codex":[0.9934669,0.0005147774,0.001291723,0.0009469891,0.00229402,0.001485606],"domain_scores_gemma":[0.9951004,0.0005147196,0.00009230257,0.001151604,0.002440143,0.0007007929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0009512174,0.0004074132,0.8326929,0.0004207869,0.0001805492,0.007367831,0.0804132,0.05196166,0.01095357,0.0002183469,0.004906327,0.009526192],"study_design_scores_gemma":[0.007667806,0.003737984,0.4323596,0.001546164,0.0001101026,0.0000361945,0.507232,0.01429335,0.01692289,0.0009261219,0.01339591,0.001771914],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961268,0.00009038308,0.0004077262,0.0004109852,0.0002146362,0.001996083,0.0003211221,0.0002590009,0.0001732906],"genre_scores_gemma":[0.9919942,0.00003249987,0.00727671,0.00002231626,0.0001219376,0.0001790073,0.0001250783,0.0001133253,0.0001349254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4268188,"threshold_uncertainty_score":0.9998607,"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."}}