{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001302769,0.0001703219,0.000148045,0.00005264871,0.0005478418,0.00002737164,0.000143989,0.00004527735,0.0004894121],"category_scores_gemma":[0.000002962116,0.0001417011,0.00003678237,0.0001523494,0.00006850734,0.00009171423,0.0006375573,0.0001240959,0.000009526628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006780433,"about_ca_system_score_gemma":0.000004357359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001374719,"about_ca_topic_score_gemma":0.002133368,"domain_scores_codex":[0.998667,0.00009391728,0.0001583674,0.0003858765,0.0003353652,0.0003594639],"domain_scores_gemma":[0.9996531,0.000009980159,0.00004189809,0.0002098311,0.000004569249,0.0000806095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002321679,0.0005875594,0.3263613,0.00009260811,0.0004042125,0.0005337703,0.0725333,0.007578308,0.431808,0.03192824,0.02568631,0.1022542],"study_design_scores_gemma":[0.000937496,0.000321789,0.203962,0.00001357399,0.00002101327,0.00008051308,0.003279256,0.002092215,0.04098899,0.003157485,0.7443889,0.0007568127],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716101,0.0005250705,0.00003899897,0.002278968,0.0007128527,0.0001066947,0.00001936197,0.00008402227,0.02462391],"genre_scores_gemma":[0.9943122,0.00002902916,0.0001173417,0.000440851,0.0003683105,0.00005428908,0.00003461585,0.00001938679,0.004623923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7187026,"threshold_uncertainty_score":0.5778402,"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."}}