{"id":"W4220931879","doi":"10.1029/2021ef002225","title":"Mapping Water, Energy and Carbon Footprints Along Urban Agglomeration Supply Chains","year":2022,"lang":"en","type":"article","venue":"Earth s Future","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Urban agglomeration; Greenhouse gas; Economies of agglomeration; Resource (disambiguation); Energy consumption; Population; Environmental science; Leverage (statistics); Natural resource economics; China; Business; Environmental engineering; Geography; Economic geography; Economics; Engineering; Economic growth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001929784,0.0003428305,0.0001373387,0.00145237,0.0002940878,0.0006275998,0.0002749148,0.0002889426,0.001979552],"category_scores_gemma":[0.0004681181,0.0001718641,0.0004664626,0.002345729,0.0002271077,0.0005757394,0.0005348795,0.0001859187,0.0002095779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072329,"about_ca_system_score_gemma":0.0006817848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05574216,"about_ca_topic_score_gemma":0.0430118,"domain_scores_codex":[0.9998989,0.00002752636,0.000003997909,0.00002476316,0.00002458072,0.00002020193],"domain_scores_gemma":[0.9997943,0.00007171475,0.00003851845,0.00002089599,0.00005455313,0.00002000891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000166453,0.0001618287,0.3805736,0.00009378306,0.0001755869,0.0003071963,0.0003366398,0.5836045,0.002721179,0.003548243,0.001070192,0.027241],"study_design_scores_gemma":[0.00001140253,0.00006084044,0.1343961,0.00001759245,0.00003979115,0.00003663803,0.0009256778,0.8589704,0.00143504,0.002294176,0.001792749,0.00001958065],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948783,0.00003132984,0.002677992,0.00004796142,0.000001347461,0.00002368272,0.0008846874,0.00004116816,0.001413534],"genre_scores_gemma":[0.9965433,0.00004793616,0.001933674,0.000004558538,8.98341e-7,0.0000226167,0.0007948769,0.000005390684,0.0006466289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05574216,"threshold_uncertainty_score":0.1108354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005360440135993649,"score_gpt":0.1640269078885975,"score_spread":0.1586664677526038,"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."}}