{"id":"W2508344553","doi":"10.3390/w8090377","title":"ResilSIM—A Decision Support Tool for Estimating Resilience of Urban Systems","year":2016,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Western University","funders":"Federal Emergency Management Agency","keywords":"Damages; Flooding (psychology); Resilience (materials science); Natural disaster; Urbanization; Flood myth; Environmental planning; Hazard; Environmental resource management; Natural hazard; Emergency management; Urban planning; Population; Government (linguistics); Decision support system; Climate change; Risk analysis (engineering); Business; Computer science; Geography; Engineering; Environmental science; Civil engineering; Political science; Economics; Economic growth","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":[0.0003917664,0.00007497577,0.0001001398,0.00002184559,0.00005875033,0.00001844222,0.0001831387,0.00002646732,0.0004841505],"category_scores_gemma":[0.00002710071,0.00003677649,0.00003533747,0.0000327177,0.00005729421,0.0002119103,0.0001594842,0.00001537076,0.0002826879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004924583,"about_ca_system_score_gemma":0.000002721271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007625594,"about_ca_topic_score_gemma":0.00001223061,"domain_scores_codex":[0.9991098,0.00001471698,0.0002186937,0.0002051765,0.000230533,0.000221062],"domain_scores_gemma":[0.9996259,0.00005764782,0.0000456917,0.0002332115,0.000007007774,0.00003052046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000216444,0.0001517188,0.2387211,0.000160961,0.00003516651,0.00001352039,0.0009190721,0.006067138,0.3250444,0.001556928,0.2948864,0.1322271],"study_design_scores_gemma":[0.006164663,0.001867244,0.1364054,0.0007940306,0.0001751574,0.0000127244,0.0003305614,0.05731832,0.3516257,0.007888295,0.4357873,0.001630699],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7856642,0.000006874249,0.2090283,0.0002148243,0.0004533608,0.000546421,0.000006723487,0.00003297329,0.004046268],"genre_scores_gemma":[0.9553443,0.000002706337,0.03629451,0.00002508934,0.00004076697,0.00004572594,0.000002014337,0.000008737982,0.008236174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1727339,"threshold_uncertainty_score":0.5301109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008609951304718925,"score_gpt":0.2459176763541761,"score_spread":0.2373077250494572,"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."}}