{"id":"W2998419536","doi":"10.14288/1.0387299","title":"Urban flood management and disaster in Canada : incidence, recovery strategy, and environmental resilience","year":2019,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flood myth; Resilience (materials science); Environmental planning; Flood risk management; Emergency management; Natural disaster; Environmental resource management; Vulnerability (computing); Disaster recovery; Geography; Business; Environmental science; Political science; Economic growth; Computer science; Computer security; Meteorology; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001281795,0.00030943,0.0004122586,0.003808711,0.006594621,0.002882135,0.001248044,0.000367572,0.003157974],"category_scores_gemma":[0.006601335,0.0002978412,0.0004989977,0.01317065,0.002058506,0.001149021,0.003203752,0.0008563449,0.0001664198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0952153,"about_ca_system_score_gemma":0.1158251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9980331,"about_ca_topic_score_gemma":0.9989557,"domain_scores_codex":[0.9985532,0.0001263687,0.00007174133,0.0001607292,0.0004746054,0.0006132874],"domain_scores_gemma":[0.9963175,0.000475603,0.000518109,0.0001434672,0.001671811,0.0008735264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000821711,0.00007221464,0.9197192,0.0001710711,0.0000886189,0.0003185861,0.01888031,0.002545487,0.0001728628,0.006584696,0.008769099,0.04259566],"study_design_scores_gemma":[0.000006455517,0.00001931606,0.9384464,0.0001253927,0.00003509133,0.0000628804,0.04617644,0.001685292,0.0001061929,0.0007089952,0.01258281,0.0000447962],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701793,0.001083706,0.0007290555,0.002214654,0.00002358502,0.0001307775,0.006003854,0.00003810292,0.01959696],"genre_scores_gemma":[0.9916443,0.001122922,0.000823287,0.0001583488,0.000004794717,0.00006499628,0.002495797,0.00001392751,0.003671576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0952153,"threshold_uncertainty_score":0.6908386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002076738556447568,"score_gpt":0.1319195669594037,"score_spread":0.1298428284029561,"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."}}