{"id":"W2732841989","doi":"10.1002/2017wr020640","title":"Water security, risk, and economic growth: Insights from a dynamical systems model","year":2017,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water resources management and optimization","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada; Department for International Development; Natural Environment Research Council; Sight Research UK","keywords":"Poverty trap; Investment (military); Natural resource economics; Water security; Poverty; Productivity; Economics; Context (archaeology); Business; Water resources; Capital (architecture); Economic growth; Geography","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":[0.0007525858,0.0006906692,0.0006891392,0.0006355052,0.0005149451,0.001770225,0.000678068,0.00154004,0.003997279],"category_scores_gemma":[0.003719955,0.0003116903,0.0006681484,0.0005412813,0.001083999,0.001300335,0.001381579,0.001352167,0.0002415548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183875,"about_ca_system_score_gemma":0.0008826568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0140863,"about_ca_topic_score_gemma":0.006176934,"domain_scores_codex":[0.9997416,0.0001269197,0.000009794529,0.00004425001,0.0000368901,0.00004048244],"domain_scores_gemma":[0.9977381,0.001696256,0.0002549886,0.00004845671,0.000130771,0.0001314048],"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":[0.00002797534,0.00005129721,0.0042027,0.00003915135,0.00003494875,0.0002373629,0.0001438072,0.9013467,0.0004184443,0.09026412,0.001174311,0.002059238],"study_design_scores_gemma":[0.00000592259,0.00001386719,0.0005860078,0.000006260479,0.000006475226,0.00001574623,0.00005095195,0.9798925,0.00002205162,0.01903352,0.0003584923,0.000008085021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6166274,0.001731594,0.286121,0.0110497,0.0002130858,0.0001188054,0.001258603,0.0002303244,0.08264951],"genre_scores_gemma":[0.9905923,0.0005022018,0.004131567,0.0001220003,0.0000448368,0.00006263383,0.0001118841,0.00001411803,0.004418319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0140863,"threshold_uncertainty_score":0.02800864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433730759109094,"score_gpt":0.2513563354702193,"score_spread":0.2270190278791284,"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."}}