{"id":"W2484752949","doi":"10.1021/acs.est.6b01065","title":"Global and Regional Evaluation of Energy for Water","year":2016,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Pacific Northwest National Laboratory; Office of Science; Battelle; U.S. Department of Energy","keywords":"Environmental science; Water-energy nexus; Energy consumption; Energy intensity; Water resource management; Farm water; Desalination; Irrigation; Water use; Agriculture; Surface water; Groundwater; Environmental engineering; Water conservation; Natural resource economics; Geography; Engineering; Economics","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":[0.001890049,0.0005284967,0.0003756849,0.002463156,0.0002022638,0.001464848,0.0004028668,0.0003629695,0.003202561],"category_scores_gemma":[0.003365213,0.000145466,0.0009243847,0.006062583,0.0004308369,0.001343911,0.001172857,0.0002593152,0.000414448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002433708,"about_ca_system_score_gemma":0.001369953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03452474,"about_ca_topic_score_gemma":0.04624883,"domain_scores_codex":[0.9989836,0.0004375931,0.00003905823,0.0001661643,0.0002765409,0.00009710489],"domain_scores_gemma":[0.9992211,0.0001697292,0.00009171396,0.0001091328,0.0003727403,0.00003555307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003082857,0.00006978055,0.1607556,0.0006821004,0.001248817,0.0003111487,0.0004367749,0.5499048,0.003021827,0.08170961,0.01344499,0.1881062],"study_design_scores_gemma":[0.00006827735,0.0003210591,0.3718639,0.0004271076,0.0009979834,0.0003761441,0.005343726,0.3543416,0.009751938,0.05222794,0.2041096,0.0001706388],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.655238,0.007486279,0.05467382,0.003162724,0.0001648903,0.0001793026,0.02784334,0.0004303674,0.2508213],"genre_scores_gemma":[0.9818232,0.001394007,0.008065326,0.00008931947,0.00001447708,0.00004839636,0.004780909,0.0001261044,0.003658327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03452474,"threshold_uncertainty_score":0.06864756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390895998415934,"score_gpt":0.2311050373730363,"score_spread":0.2171960773888769,"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."}}