{"id":"W4313442997","doi":"10.1016/j.ijdrr.2022.103496","title":"Resource-based seismic resilience optimization of the blocked urban road network in emergency response phase considering uncertainties","year":2022,"lang":"en","type":"article","venue":"International Journal of Disaster Risk Reduction","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Resilience (materials science); Emergency response; Phase (matter); Computer science; Resource (disambiguation); Environmental science; Materials science; Chemistry; Computer network; Medical emergency","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008990052,0.000109261,0.0001740863,0.0002970846,0.0001201724,0.00002145713,0.0003807676,0.00003430992,0.0002468246],"category_scores_gemma":[0.0001819198,0.00009299896,0.0001671527,0.0004812748,0.00008137748,0.0001991685,0.00005226644,0.0003950401,3.573525e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002966782,"about_ca_system_score_gemma":0.00008061525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004165608,"about_ca_topic_score_gemma":0.000008950597,"domain_scores_codex":[0.9980314,0.0004321184,0.000722858,0.0001150286,0.0005609,0.000137677],"domain_scores_gemma":[0.9991236,0.00006092873,0.0004455746,0.0001658198,0.000173231,0.00003087005],"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":[0.000811546,0.00005810815,0.005943387,0.000006682532,0.00007241995,0.00000372313,0.001389741,0.9854473,0.002278664,0.000007550492,0.0005315624,0.003449256],"study_design_scores_gemma":[0.001155725,0.0001578242,0.004983067,0.00008081266,0.0000701803,0.00009159166,0.003972768,0.9844884,0.003247996,0.0005201428,0.00108212,0.0001493671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9717543,0.0002312502,0.02602334,0.0003958335,0.001445719,0.00008708873,0.00001323957,0.00001126228,0.00003800786],"genre_scores_gemma":[0.9990669,0.00005079055,0.0005930491,0.00001274707,0.0002239029,0.000006463481,0.000004419337,0.00001173909,0.00002997527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02731266,"threshold_uncertainty_score":0.3792388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0061587094313815,"score_gpt":0.2396800087900713,"score_spread":0.2335212993586898,"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."}}