{"id":"W2530064535","doi":"10.1016/j.uclim.2015.07.002","title":"Building capacity for climate change adaptation in urban areas: Editors’ introduction","year":2015,"lang":"en","type":"article","venue":"Urban Climate","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Climate change; Adaptation (eye); Urban climate; Climate change adaptation; Environmental planning; Environmental science; Environmental resource management; Adaptive capacity; Geography; Urban planning; Ecology; Civil engineering; Engineering; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007382422,0.0002873236,0.0004532874,0.0008053328,0.0002393237,0.0003364106,0.0004561215,0.0001997192,0.00002980716],"category_scores_gemma":[0.002509996,0.000254958,0.0001746347,0.001187556,0.00009317902,0.001230147,0.000100952,0.0002240723,0.0001336533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001657773,"about_ca_system_score_gemma":0.00004517661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001278764,"about_ca_topic_score_gemma":0.0001791095,"domain_scores_codex":[0.9956889,0.0002569549,0.001107361,0.0009173644,0.001249485,0.0007798999],"domain_scores_gemma":[0.9973007,0.0005090057,0.0005285601,0.0006277042,0.0007844275,0.00024964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002752559,0.001279346,0.2704781,0.0004138319,0.0001012143,0.00002627788,0.04839346,0.02546396,0.002272482,0.1365715,0.3349347,0.1773126],"study_design_scores_gemma":[0.00439143,0.0007108136,0.01896082,0.0002219609,0.0001117471,0.00002414722,0.009582204,0.6251178,0.0004040971,0.09376322,0.2454159,0.00129581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9238477,0.0004047519,0.05183968,0.003698281,0.01689942,0.001501683,0.0002649603,0.0003792435,0.001164247],"genre_scores_gemma":[0.9698521,0.00009006528,0.01810213,0.0002208863,0.01120266,0.0003228077,0.00009434338,0.00004971049,0.00006532115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5996538,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2421453335540386,"score_gpt":0.3691247132415552,"score_spread":0.1269793796875165,"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."}}