{"id":"W4220673689","doi":"10.1016/j.scs.2022.103855","title":"Sustainability-oriented maintenance management of highway bridge networks based on Q-learning","year":2022,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Bridge (graph theory); Sustainability; Bridge maintenance; Transport engineering; Computer science; Asset management; Engineering; Environmental economics; Business; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001317888,0.0003356746,0.0005932421,0.0006844234,0.000547442,0.001118551,0.00107844,0.0006546052,0.002755342],"category_scores_gemma":[0.002885395,0.0001676766,0.0004294669,0.0004827082,0.000446918,0.001330626,0.0009282173,0.0004034571,0.0002120693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048253,"about_ca_system_score_gemma":0.001874827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008237461,"about_ca_topic_score_gemma":0.007830964,"domain_scores_codex":[0.99949,0.0001499465,0.00002886403,0.0001257917,0.0001006081,0.0001047768],"domain_scores_gemma":[0.9981969,0.0008731151,0.0002161567,0.0001180709,0.0004570232,0.0001386166],"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":[0.0001935004,0.0005055375,0.008412116,0.00009196684,0.00005285403,0.00009156951,0.0001889996,0.7993923,0.003689736,0.007150954,0.001272099,0.1789584],"study_design_scores_gemma":[0.000008281145,0.00004557193,0.0007755179,0.0000042153,0.00001042339,0.000007943851,0.00003259688,0.9962479,0.0003999359,0.002272285,0.000191455,0.000003744684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1937076,0.0001742813,0.798215,0.0003601048,0.00004214471,0.0002028054,0.00007364946,0.0005817577,0.006642703],"genre_scores_gemma":[0.9755882,0.00004125065,0.02351341,0.0000280492,0.00001002637,0.00004412468,0.00004662931,0.00001159613,0.0007167694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008237461,"threshold_uncertainty_score":0.016379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002518141150455459,"score_gpt":0.1843289332393781,"score_spread":0.1818107920889226,"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."}}