{"id":"W2885135538","doi":"10.3390/su10082822","title":"Blue and Green Water Footprint Assessment for China—A Multi-Region Input–Output Approach","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Hohai University","keywords":"Virtual water; China; Water use; Sustainability; Footprint; Agricultural economics; Business; Natural resource economics; Production (economics); Agriculture; Environmental science; Environmental resource management; Geography; Water scarcity; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.001101123,0.000327829,0.0003031083,0.00004177744,0.0005019596,0.00006734557,0.0002950396,0.0001639358,0.0001349377],"category_scores_gemma":[0.00018411,0.0002415373,0.0001313161,0.0001212547,0.001558534,0.0002845991,0.0008325803,0.0002087427,0.00001320381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002461288,"about_ca_system_score_gemma":0.00004932913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001874051,"about_ca_topic_score_gemma":0.0001304825,"domain_scores_codex":[0.9974331,0.0001871649,0.0003606075,0.0008810491,0.0002922092,0.0008458195],"domain_scores_gemma":[0.9987904,0.00004249576,0.00006985033,0.0007444345,0.00006430026,0.0002884826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002031822,0.001180673,0.9378984,0.0003782483,0.00003013457,0.000007224899,0.007008723,0.000330648,0.0006693188,0.0004684194,0.0001327655,0.05169227],"study_design_scores_gemma":[0.001037477,0.0005287602,0.9674742,0.000002717751,0.00002950398,0.00001218162,0.002189948,0.006775105,0.002105213,0.01476791,0.004673195,0.0004038257],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9428099,0.000009222739,0.05255739,0.001389384,0.00006689598,0.002131199,0.00000584937,0.00007517398,0.0009550129],"genre_scores_gemma":[0.9915403,0.000002478666,0.00572249,0.0001329216,0.00008077696,0.0002469248,0.00001681835,0.00002965064,0.002227609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05128845,"threshold_uncertainty_score":0.9849605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407856050096629,"score_gpt":0.2771991685194088,"score_spread":0.2631206080184425,"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."}}