{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003483934,0.000470736,0.0006092666,0.0001701798,0.0001451695,0.000187599,0.000618457,0.0001372975,0.007695867],"category_scores_gemma":[0.00000158545,0.0002328053,0.0002620765,0.00007623505,0.00004134907,0.0006169722,0.001086884,0.0002687701,0.01552695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008723493,"about_ca_system_score_gemma":0.000001459081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001959626,"about_ca_topic_score_gemma":0.00001650059,"domain_scores_codex":[0.9975632,0.00009569463,0.0006981619,0.0005952667,0.0002408712,0.0008068417],"domain_scores_gemma":[0.9989182,0.00001071434,0.0000311947,0.0008814762,0.0000341881,0.0001241775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002954138,0.0003120594,0.003197719,0.002337175,0.0006874764,0.0001837005,0.07856166,0.1569684,0.2954522,0.00004027172,0.4385776,0.0233863],"study_design_scores_gemma":[0.0007098539,0.0001390849,0.0001446683,0.0007129821,0.0001705022,0.00001020844,0.0005290138,0.01417217,0.2100788,0.001302776,0.7708162,0.001213744],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653462,0.002412581,0.002664615,0.004513142,0.002218035,0.001614894,0.00002760787,0.0006232767,0.02057968],"genre_scores_gemma":[0.9870589,0.001180922,0.0004874569,0.0003145296,0.0003289882,0.0001494189,0.0007458256,0.0001103681,0.009623588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3322386,"threshold_uncertainty_score":0.9932112,"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."}}