{"id":"W1537518880","doi":"10.1002/2014ef000253","title":"Positioning infrastructure and technologies for low‐carbon urbanization","year":2014,"lang":"en","type":"article","venue":"Earth s Future","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Atmospheric Research","keywords":"Urbanization; Greenhouse gas; Interdependence; Fossil fuel; Business; Natural resource economics; Electricity; Environmental planning; Environmental economics; Environmental resource management; Environmental science; Engineering; Economics; Economic growth; Political science; Ecology; Waste management","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":[0.00008685276,0.00008949047,0.00007521237,0.00001464999,0.0001266841,0.00002594087,0.00006602052,0.0001042866,0.00009643309],"category_scores_gemma":[0.0000489209,0.00007553644,0.00001887857,0.00008102435,0.0001183877,0.0001217154,0.00007450362,0.0000841858,0.000004912985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003475416,"about_ca_system_score_gemma":0.000001812312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007102435,"about_ca_topic_score_gemma":0.00001172049,"domain_scores_codex":[0.9994863,0.00001636829,0.00007496015,0.0001783784,0.00008789866,0.0001561557],"domain_scores_gemma":[0.9997776,0.00001607095,0.00003224881,0.000137621,0.000003079294,0.0000333521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004317513,0.00004419974,0.8407065,0.00006511901,0.00001034074,0.000001176273,0.001132473,0.001322589,0.01576524,0.001767673,0.001464821,0.1376767],"study_design_scores_gemma":[0.0003188293,0.0001411654,0.9546342,0.000007085416,0.000009610927,0.000006524815,0.0007809477,0.003730616,0.005500597,0.00759134,0.02710137,0.0001776581],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952959,0.00005961202,0.001374053,0.00076406,0.00008079025,0.0002256853,0.000003241133,0.00009339347,0.002103209],"genre_scores_gemma":[0.9962767,0.00001259647,0.003233986,0.0001438604,0.00008074845,0.000009448143,0.00001130536,0.000008132104,0.0002231938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.137499,"threshold_uncertainty_score":0.3080287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001232788377151549,"score_gpt":0.1747114159067243,"score_spread":0.1734786275295728,"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."}}