{"id":"W4221040522","doi":"10.1016/j.jconhyd.2022.103985","title":"Planning regional-scale water-energy-food nexus system management under uncertainty: An inexact fractional programming method","year":2022,"lang":"en","type":"article","venue":"Journal of Contaminant Hydrology","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; Ministry of Science and Technology of the People's Republic of China; Royal Society","keywords":"Nexus (standard); Agriculture; Sowing; Agricultural engineering; Unit (ring theory); Environmental science; Scale (ratio); Water resources; Water resource management; Mathematics; Environmental economics; Computer science; Agronomy; Engineering; Economics; Geography; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002113325,0.0007795425,0.001587528,0.000889731,0.0007019292,0.001686146,0.001361867,0.001899841,0.003218555],"category_scores_gemma":[0.005810493,0.0009082897,0.000871159,0.0009571403,0.001273057,0.001427219,0.00139953,0.001399733,0.0001354682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297809,"about_ca_system_score_gemma":0.002154915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02040525,"about_ca_topic_score_gemma":0.01559323,"domain_scores_codex":[0.999436,0.0002813252,0.00001964407,0.0001038013,0.00007170006,0.0000874602],"domain_scores_gemma":[0.9970801,0.002466004,0.0001492128,0.000052591,0.000158245,0.0000939015],"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":[0.00001180966,0.000008921066,0.00009546243,0.00001243049,0.000009651696,0.00001991481,0.000009069263,0.9947165,0.00004551316,0.002575318,0.000131028,0.002364473],"study_design_scores_gemma":[0.000003637778,0.000005468906,0.00001653421,0.000002179334,0.000002999524,0.000001872056,0.000006168255,0.9981547,0.00002063093,0.001704137,0.00008034054,0.000001440755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05376475,0.0004427118,0.937386,0.0009193827,0.0001072782,0.00006648259,0.0001968698,0.0001538121,0.006962736],"genre_scores_gemma":[0.8216739,0.0003915536,0.1733123,0.0001991385,0.00008973481,0.0002252192,0.0001804827,0.000093394,0.003834429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02040525,"threshold_uncertainty_score":0.040573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01802879253400488,"score_gpt":0.2536430772847646,"score_spread":0.2356142847507597,"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."}}