{"id":"W4230761415","doi":"10.5194/hessd-8-483-2011","title":"Internal and external green-blue agricultural water footprints of nations, and related water and land savings through trade","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Institute for Applied Systems Analysis; Bundesministerium für Bildung und Forschung","keywords":"Virtual water; Agriculture; Water use; Farm water; Environmental science; Non-revenue water; Irrigation; Water resource management; Population; Business; Agricultural economics; Water conservation; Geography; Water scarcity; Economics; Agronomy; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002323445,0.0002942824,0.00030363,0.0000333525,0.0001060631,0.00005403018,0.0001676051,0.0002376843,0.001177956],"category_scores_gemma":[0.00001133203,0.0001577116,0.00005480419,0.00002609812,0.0006396668,0.0002897061,0.001795732,0.0003566129,0.00001094373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001112372,"about_ca_system_score_gemma":0.000002661561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005173985,"about_ca_topic_score_gemma":0.0002556505,"domain_scores_codex":[0.998507,0.0000711379,0.0003701483,0.0005202237,0.0002189462,0.0003125875],"domain_scores_gemma":[0.9995485,0.00002102306,0.00007758632,0.0002120718,0.000005214488,0.0001356329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008549623,0.0001927882,0.9177658,0.0002841393,0.0001049337,0.00002285652,0.0294181,0.00003453457,0.04795258,0.000144511,0.00005677741,0.003937504],"study_design_scores_gemma":[0.0004880689,0.00009037693,0.9356,0.00005602642,0.0000525622,0.0001099839,0.0004639627,0.0000420424,0.05247154,0.01011844,0.0001967272,0.0003102006],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914851,0.0001386468,0.00004623819,0.0003692807,0.00007064037,0.0003555416,0.000009211177,0.00002143052,0.00750392],"genre_scores_gemma":[0.9965152,0.0002045316,0.0006656917,0.00004978409,0.00001458176,0.00001185925,0.00002101974,0.00001395986,0.002503424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02895414,"threshold_uncertainty_score":0.9997351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009700930126989745,"score_gpt":0.2161550875500328,"score_spread":0.2064541574230431,"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."}}