{"id":"W3130493259","doi":"10.1007/s00382-021-05644-9","title":"Ensemble projection of city-level temperature extremes with stepwise cluster analysis","year":2021,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Downscaling; Environmental science; Climatology; Climate change; Representative Concentration Pathways; GCM transcription factors; Baseline (sea); Cluster (spacecraft); Global warming; Scale (ratio); Ensemble average; Climate model; Mean radiant temperature; Greenhouse gas; General Circulation Model; Geography; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003479187,0.0003786357,0.0004096598,0.0005491012,0.0004363109,0.0004887594,0.0005356501,0.000323853,0.002321266],"category_scores_gemma":[0.001229934,0.000266164,0.0009419973,0.001275012,0.0001344087,0.000368243,0.0004953326,0.0006097871,0.0005140136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004320639,"about_ca_system_score_gemma":0.0014324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04945944,"about_ca_topic_score_gemma":0.04961653,"domain_scores_codex":[0.99985,0.0000449965,0.000007168699,0.00003756563,0.00002887283,0.00003143522],"domain_scores_gemma":[0.9995193,0.000112155,0.00002991791,0.0001066701,0.0002015898,0.00003032929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001062135,0.00004934832,0.01210872,0.00001926765,0.0001674056,0.00003189052,0.00005245437,0.9601704,0.001187435,0.00211357,0.002301881,0.02169143],"study_design_scores_gemma":[0.000006551493,0.000006101493,0.006219192,0.000001692031,0.00001300452,0.000004059702,0.00001327156,0.9923724,0.0003311483,0.0007154336,0.0003087837,0.000008310702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8407934,0.00008797884,0.1477037,0.0001433362,0.00008949531,0.00005762073,0.006229886,0.0008347885,0.004059935],"genre_scores_gemma":[0.9624535,0.00005231201,0.03131817,0.000008960786,0.00001421034,0.00006171485,0.005031963,0.00007474807,0.0009844155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04945944,"threshold_uncertainty_score":0.09834307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925324890803727,"score_gpt":0.2380529821806052,"score_spread":0.2187997332725679,"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."}}