{"id":"W2962338216","doi":"10.1016/j.watres.2019.07.015","title":"Can virtual water trade save water resources?","year":2019,"lang":"en","type":"article","venue":"Water Research","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":107,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; Rutgers, The State University of New Jersey; National Science Foundation","keywords":"China; Virtual water; Natural resource economics; Value (mathematics); Business; Economics; Agricultural economics; International trade; Water resources; Water scarcity; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001926656,0.0002176407,0.0002071772,0.00008817025,0.0003156892,0.0001316626,0.0005816461,0.0001433487,0.03654163],"category_scores_gemma":[0.00001250964,0.0001055216,0.00009674903,0.00008627214,0.0006414002,0.0002987699,0.001272154,0.0005905104,0.02221719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007187868,"about_ca_system_score_gemma":0.000004367636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001506401,"about_ca_topic_score_gemma":0.0001144633,"domain_scores_codex":[0.9957588,0.0004127025,0.0002558214,0.0006236029,0.001183385,0.001765676],"domain_scores_gemma":[0.9988895,0.00001825697,0.000008454608,0.000745353,0.000006291968,0.0003321353],"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":[0.000128872,0.0001900585,0.1115995,0.00002400279,0.00001537178,0.00005650241,0.0253086,0.0002580258,0.8594186,0.00001544365,0.001468521,0.001516426],"study_design_scores_gemma":[0.0005572029,0.0004409229,0.0378051,0.000005587762,0.00000424057,0.00001217949,0.001635067,0.0001048039,0.8002236,0.001380491,0.1575273,0.000303496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975866,0.00000409231,0.000001873123,0.005700096,0.0000817383,0.0006086874,0.000005906743,0.00003939903,0.01769222],"genre_scores_gemma":[0.9547727,0.000003703664,0.00001862027,0.0002611211,0.0000613403,0.00003613541,0.000043123,0.00003858084,0.04476469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1560587,"threshold_uncertainty_score":0.9785441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387708674472649,"score_gpt":0.2901500279870721,"score_spread":0.2662729412423456,"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."}}