{"id":"W2912140230","doi":"10.1017/sus.2018.16","title":"Upscaling urban data science for global climate solutions","year":2019,"lang":"en","type":"article","venue":"Global Sustainability","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"Future Earth","funders":"Engineering and Physical Sciences Research Council","keywords":"Comparability; Climate change; Big data; Data science; Scale (ratio); Computer science; Environmental planning; Environmental resource management; Environmental science; Geography; Cartography; Ecology","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":[0.02601826,0.001382296,0.001077302,0.005474501,0.001140203,0.01029397,0.003285846,0.001272963,0.01042487],"category_scores_gemma":[0.06192829,0.0009710144,0.002044436,0.008302109,0.002196234,0.0120107,0.008633777,0.003249368,0.004278067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004394729,"about_ca_system_score_gemma":0.007884058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04627718,"about_ca_topic_score_gemma":0.04063539,"domain_scores_codex":[0.9887101,0.006231911,0.0007829762,0.001076772,0.002759882,0.0004383215],"domain_scores_gemma":[0.9337686,0.02458678,0.001468139,0.0215748,0.0154572,0.00314455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000390975,0.0004515189,0.0468337,0.002414009,0.000954977,0.0002030148,0.001617687,0.1143896,0.00422912,0.08763046,0.1611489,0.5797361],"study_design_scores_gemma":[0.0001699374,0.0002610936,0.01957188,0.002490238,0.0003722425,0.00009157462,0.006879855,0.2602296,0.01080601,0.1989687,0.4998566,0.0003021028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1493178,0.02286297,0.4811639,0.1554843,0.008786565,0.003009252,0.07471314,0.02679235,0.07786977],"genre_scores_gemma":[0.3739406,0.01075527,0.5543069,0.003220566,0.001207374,0.001055166,0.04672015,0.003163129,0.00563081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04627718,"threshold_uncertainty_score":0.1375993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01635048374536669,"score_gpt":0.2847964667398407,"score_spread":0.268445982994474,"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."}}