{"id":"W2803673674","doi":"10.1111/mice.12370","title":"Optimization for Roads' Construction: Selection, Prioritization, and Scheduling","year":2018,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Gantt chart; Computer science; Mathematical optimization; Scheduling (production processes); Integer programming; Linear programming; Chart; Selection (genetic algorithm); Operations research; Machine learning; Engineering; Mathematics; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00122763,0.001187253,0.001689663,0.001573968,0.000775462,0.001682583,0.001522082,0.001200145,0.008029628],"category_scores_gemma":[0.002744386,0.0007314027,0.0008267944,0.002039491,0.000646911,0.001360171,0.0007977015,0.0008907125,0.0007447341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001523283,"about_ca_system_score_gemma":0.00321824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01804523,"about_ca_topic_score_gemma":0.0197849,"domain_scores_codex":[0.9990915,0.0003375746,0.00003338701,0.000139882,0.0001487272,0.0002488941],"domain_scores_gemma":[0.998576,0.0008587243,0.0001501985,0.00007727693,0.0001875869,0.0001501136],"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":[0.0002059058,0.000147041,0.001297629,0.0001114318,0.00003908678,0.00005554805,0.0000464194,0.9356727,0.00135756,0.003757023,0.003090036,0.05421966],"study_design_scores_gemma":[0.00002837301,0.00006696043,0.0007719136,0.000006061668,0.00001841275,0.00002187688,0.00004111793,0.9950631,0.0006286317,0.002494606,0.0008499856,0.000008880451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1830244,0.001329093,0.7947938,0.001457133,0.0002102082,0.0005040973,0.00115076,0.001808938,0.01572152],"genre_scores_gemma":[0.8203931,0.0005265179,0.1713508,0.0001216948,0.00008513734,0.0001924482,0.0007420339,0.0002720817,0.006316212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01804523,"threshold_uncertainty_score":0.03588039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002601620024809909,"score_gpt":0.1761454539143176,"score_spread":0.1735438338895077,"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."}}