{"id":"W2907022776","doi":"10.1016/j.scitotenv.2018.12.433","title":"Transfer of virtual water embodied in food: A new perspective","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Virtual water; Nexus (standard); Water resources; Water scarcity; Natural resource economics; Business; Agriculture; Consumption (sociology); Competition (biology); China; Water use; Farm water; Food systems; Water conservation; Environmental economics; Water resource management; Environmental science; Environmental resource management; Economics; Food security; Ecology; Geography; Computer 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005369501,0.0001593453,0.0001831286,0.00004215563,0.0002401418,0.000008212493,0.0009821919,0.00003071781,0.000731469],"category_scores_gemma":[0.00002071334,0.00007558956,0.00008825538,0.0002700947,0.005459142,0.0002366658,0.001002833,0.00009521621,0.0001185956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003360165,"about_ca_system_score_gemma":0.0000198348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210248,"about_ca_topic_score_gemma":0.00009374745,"domain_scores_codex":[0.9980859,0.00006937653,0.0002585083,0.0003637852,0.0007988761,0.0004236102],"domain_scores_gemma":[0.9992818,0.00002556621,0.00004830407,0.0005710081,0.000005758831,0.00006756734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008572602,0.000196478,0.00006293642,0.000002013187,0.00002646634,5.315574e-7,0.01771198,0.01841314,0.9379809,0.02475303,0.0001256667,0.0006410995],"study_design_scores_gemma":[0.000386021,0.0007053316,0.01366799,0.00001457479,0.0000181924,0.000005326056,0.002140193,0.0004239714,0.9489353,0.03345571,0.00009181043,0.0001555365],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625748,0.00002361706,0.00005030658,0.002369667,0.000134813,0.0002166988,0.000007303051,0.00000625731,0.03461648],"genre_scores_gemma":[0.9982213,0.000003864652,0.00008673048,0.00004220246,0.00003166084,0.00000864519,1.067529e-7,0.000009039778,0.00159644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03564645,"threshold_uncertainty_score":0.9972475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147526510358872,"score_gpt":0.1990903241999157,"score_spread":0.1876150590963269,"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."}}