{"id":"W2206725513","doi":"10.1007/s12665-015-4484-6","title":"A path-based structural decomposition analysis of Beijing’s water footprint evolution","year":2015,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; Ministry of Science and Technology of the People's Republic of China","keywords":"Beijing; Restructuring; Virtual water; Environmental science; Decomposition; Water use; Environmental engineering science; Footprint; Environmental engineering; Water resources; Chemistry; Biogeosciences; Business; Geology; Geography; Water scarcity; China; Earth science","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":[],"category_scores_codex":[0.000680377,0.0001984597,0.0002543993,0.000143349,0.000243865,0.00003684222,0.0003133256,0.00006654516,0.002074596],"category_scores_gemma":[0.0000156092,0.0001455281,0.0001783097,0.0004528436,0.001538568,0.0003646832,0.0002563581,0.00009531772,0.0001293496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003623996,"about_ca_system_score_gemma":0.00001536642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005471328,"about_ca_topic_score_gemma":0.0001261271,"domain_scores_codex":[0.997743,0.0001255306,0.0003404558,0.0004799039,0.0008704499,0.0004407304],"domain_scores_gemma":[0.9993259,0.00002510324,0.000113029,0.0002926251,0.000002292466,0.0002411061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003040676,0.0001139067,0.8748059,0.000003188476,0.00003148642,0.000002738434,0.0005879873,0.08252691,0.04110945,0.0000298513,0.000006151542,0.0007519813],"study_design_scores_gemma":[0.0003374105,0.0003482456,0.9289717,0.000003167992,0.0001287598,0.000002626971,0.000851498,0.0405219,0.02799614,0.0004562452,0.0001590669,0.0002232558],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969397,0.00003514755,0.0009540226,0.00009828606,0.00008106628,0.0002192876,0.00001991884,0.00002415266,0.001628484],"genre_scores_gemma":[0.9975232,0.000001720442,0.002214233,0.00007192251,0.00001192842,0.000009330037,0.00004140879,0.00000735046,0.0001189321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05416574,"threshold_uncertainty_score":0.9988376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01037867603730468,"score_gpt":0.2496902625080169,"score_spread":0.2393115864707122,"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."}}