{"id":"W2966244126","doi":"10.1002/wat2.1376","title":"Water resilience lessons from Cape Town's water crisis","year":2019,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Water","topic":"Water resources management and optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; University of British Columbia; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Cape; Resilience (materials science); Corporate governance; Psychological resilience; Water supply; Politics; Water scarcity; Water sector; Face (sociological concept); Environmental planning; Geography; Business; Political science; Sociology; Environmental science; Environmental engineering; Archaeology; Social science","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":[0.001343451,0.0003830365,0.0003044998,0.0007274242,0.009003459,0.005996,0.001227209,0.002770826,0.008888335],"category_scores_gemma":[0.004229065,0.0003313184,0.000231508,0.001038415,0.008418226,0.003716726,0.005505448,0.003346418,0.0004164191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01157552,"about_ca_system_score_gemma":0.006748779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1400272,"about_ca_topic_score_gemma":0.2112598,"domain_scores_codex":[0.9989581,0.0004087194,0.00002891166,0.00009578525,0.0001312086,0.0003772086],"domain_scores_gemma":[0.9982187,0.0007337396,0.0001829785,0.0001222744,0.0001796603,0.0005625021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002817756,0.0001928734,0.006066262,0.0009289692,0.00006964996,0.04123381,0.2539633,0.006513787,0.00469984,0.35663,0.2534983,0.07592158],"study_design_scores_gemma":[0.00004869519,0.0001191336,0.01136668,0.0008520211,0.00002710482,0.001775382,0.2503586,0.001256732,0.001424477,0.03965759,0.6929893,0.0001241931],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4258035,0.008832988,0.002509999,0.3653055,0.001366947,0.0001428548,0.0003008598,0.0001119538,0.1956255],"genre_scores_gemma":[0.976762,0.002572658,0.0005172523,0.00489979,0.0001332379,0.00004190261,0.00003198867,0.00003169086,0.01500951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1400272,"threshold_uncertainty_score":0.2784243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01518149934373397,"score_gpt":0.2531941924447764,"score_spread":0.2380126931010424,"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."}}