{"id":"W4229813510","doi":"10.1109/wsc.2017.8248049","title":"Data-driven simulation-based model for planning roadway operation and maintenance projects","year":2017,"lang":"en","type":"article","venue":"2017 Winter Simulation Conference (WSC)","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Truck; Snow removal; Snow; Computer science; Operational planning; Resource (disambiguation); Data modeling; Operations research; Transport engineering; Real-time computing; Simulation; Engineering; Automotive engineering; Meteorology; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003022656,0.0001996023,0.0001974604,0.00004639528,0.0006799655,0.0005321939,0.0005398821,0.0001072818,0.0001767404],"category_scores_gemma":[0.0003922609,0.0001897245,0.00003310644,0.00001956182,0.0002223868,0.001736743,0.000294963,0.00007672007,0.00007225294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007924208,"about_ca_system_score_gemma":0.0000514188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009788707,"about_ca_topic_score_gemma":0.0004208099,"domain_scores_codex":[0.9985792,0.00003798425,0.0003062314,0.0006005503,0.0002217801,0.0002542639],"domain_scores_gemma":[0.9984109,0.0001504608,0.0003353504,0.0009603919,0.00006298794,0.00007984083],"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":[0.00009231019,0.00001483828,0.0470917,0.00001330978,0.000007933499,7.395921e-7,0.0003324755,0.9491283,0.00138519,0.00007489315,0.0002349559,0.001623321],"study_design_scores_gemma":[0.0008541221,0.00004060355,0.04140082,0.00005622724,0.00002178962,9.511273e-7,0.00002865668,0.9558843,0.0002028966,0.0003711007,0.0009109714,0.0002275317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4202828,0.000002192793,0.5779453,0.0001998884,0.0003056726,0.000615277,0.0001162,0.00005195202,0.0004807028],"genre_scores_gemma":[0.9911291,9.47076e-7,0.008169492,0.0001112042,0.0001082601,0.00004093062,0.0001991849,0.00002114357,0.0002197659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5708463,"threshold_uncertainty_score":0.7736741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.143616142391013,"score_gpt":0.3498062278500631,"score_spread":0.2061900854590501,"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."}}