{"id":"W4377263933","doi":"10.1016/j.jenvman.2023.117991","title":"Water risk modeling: A framework for finance","year":2023,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Global Risk Institute in Financial Services","keywords":"Diversification (marketing strategy); Water security; Risk management; Climate change; Business; Risk analysis (engineering); Risk assessment; Environmental science; Water resources; Environmental resource management; Finance; Computer science; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002024865,0.0001063321,0.0001170101,0.0001626059,0.00005526875,0.00003162421,0.0001558024,0.00003273013,0.00004439257],"category_scores_gemma":[0.000002120795,0.00008315314,0.00009842021,0.0000653744,0.00001025341,0.0001289519,0.00006827596,0.00009226921,0.00008633269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006207441,"about_ca_system_score_gemma":2.907726e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.905518e-7,"about_ca_topic_score_gemma":8.579704e-8,"domain_scores_codex":[0.9992574,0.000008542676,0.0002653483,0.00008431021,0.0001770453,0.0002073116],"domain_scores_gemma":[0.9997894,0.00001060248,0.00005205908,0.0001153085,0.000002760696,0.00002982634],"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":[0.00001988205,0.00002188516,0.0001332147,0.0000491757,0.0001149817,0.00001451579,0.0002158235,0.9907379,0.00008525659,0.00020952,0.001266796,0.007131025],"study_design_scores_gemma":[0.0006619222,0.00009298047,0.0008721334,0.00005222367,0.0001254926,0.000001857936,0.0003065779,0.9450535,0.000612927,0.009788419,0.04224478,0.0001872296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4572192,0.0001501409,0.540974,0.0001073182,0.0004305438,0.0002933331,0.000008440854,0.00007325272,0.0007437434],"genre_scores_gemma":[0.9767843,0.002296966,0.01985671,0.00002942576,0.0001330113,0.00001795285,0.00001275011,0.00003551132,0.0008333302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5211173,"threshold_uncertainty_score":0.3390887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008278817348118962,"score_gpt":0.183905033019471,"score_spread":0.175626215671352,"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."}}