{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007540646,0.0003416536,0.0001658967,0.001512773,0.001942887,0.004629419,0.0005671491,0.001705955,0.007700309],"category_scores_gemma":[0.001932365,0.0001813152,0.0002275694,0.001826232,0.005099884,0.004963121,0.003373798,0.001393631,0.0007988365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005160864,"about_ca_system_score_gemma":0.004061589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007981997,"about_ca_topic_score_gemma":0.01402583,"domain_scores_codex":[0.9992168,0.000273201,0.00002292762,0.0001011553,0.0002102121,0.0001757118],"domain_scores_gemma":[0.9991401,0.0002806964,0.000198115,0.00007875459,0.0002112079,0.00009116936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00001526498,0.00002544058,0.007342679,0.0001553159,0.00001120577,0.0001835371,0.001592259,0.007360957,0.001511202,0.9434556,0.003102762,0.03524372],"study_design_scores_gemma":[0.00000924065,0.00007677879,0.01668788,0.0005780479,0.00004071535,0.0003147505,0.01319378,0.01115202,0.004230422,0.6796694,0.2739764,0.0000705919],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2194599,0.01310399,0.1271719,0.06238105,0.0004486446,0.000140896,0.0004427431,0.0003209143,0.5765298],"genre_scores_gemma":[0.979799,0.003852329,0.01047096,0.0003550324,0.00005395257,0.00004375484,0.0000685704,0.00002266134,0.005333756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007981997,"threshold_uncertainty_score":0.03744489,"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."}}