{"id":"W630811522","doi":"","title":"Optimization of Winter Road Maintenance Under Traffic and Weather Information","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Flexibility (engineering); Transport engineering; Snow removal; Snow; Storm; Visibility; Environmental science; Meteorology; Computer science; Engineering; Geography; Automotive engineering; Mathematics","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":[0.004968772,0.0002972899,0.0003853296,0.0006841227,0.000353041,0.000138042,0.000408581,0.0002654277,0.0008057554],"category_scores_gemma":[0.0003439222,0.0002914822,0.000100882,0.00153829,0.001417802,0.002333469,0.00003006592,0.0006516727,0.0002199591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00030139,"about_ca_system_score_gemma":0.0002007559,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00964599,"about_ca_topic_score_gemma":0.006848903,"domain_scores_codex":[0.9934193,0.0006998713,0.001122419,0.0006168929,0.003173525,0.0009679336],"domain_scores_gemma":[0.9971306,0.0002348318,0.0002733482,0.0004144392,0.001381392,0.0005653966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003745752,0.0004429539,0.3403566,0.0004873514,0.0001371109,0.0000384903,0.03482225,0.5758575,0.004294733,0.002841536,0.02166693,0.0153088],"study_design_scores_gemma":[0.003956947,0.001112914,0.9389168,0.0002575767,0.00005150397,0.000003987861,0.02593198,0.008688747,0.002486503,0.001494355,0.01640583,0.0006928764],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991098,0.00004288061,0.004306712,0.001123178,0.0002207499,0.001239099,0.0001712784,0.000138628,0.00165943],"genre_scores_gemma":[0.9927878,0.0001269229,0.00615997,0.00007963222,0.00006604425,0.0001776866,0.0002880321,0.00004852256,0.0002654125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5985602,"threshold_uncertainty_score":0.9999537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03426663962622196,"score_gpt":0.3120881005603759,"score_spread":0.277821460934154,"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."}}