{"id":"W4389903542","doi":"10.1002/wcc.872","title":"Russia in a changing climate","year":2023,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Climate Change","topic":"Russia and Soviet political economy","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"University of North Carolina at Chapel Hill; Virginia Commonwealth University; Social Sciences and Humanities Research Council of Canada; Kent State University; University of Notre Dame; University of Toronto; George Washington University","keywords":"Climate change; Political science; Global warming; Population; Greenhouse gas; Government (linguistics); Arable land; Discipline; Geography; Arctic; Political economy of climate change; Politics; Natural resource economics; Economy; Environmental planning; Environmental resource management; Economics; Agriculture; Ecology; Sociology","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":[0.00103369,0.0001521273,0.0002766837,0.0005227609,0.001235908,0.002996673,0.0002647541,0.001482166,0.003089363],"category_scores_gemma":[0.001112769,0.00008347988,0.0002502388,0.0008680426,0.002339602,0.001905139,0.0021501,0.001433395,0.000412565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001733762,"about_ca_system_score_gemma":0.002542082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003066578,"about_ca_topic_score_gemma":0.003266353,"domain_scores_codex":[0.999226,0.0004103136,0.00002944093,0.00008362198,0.000123702,0.0001269766],"domain_scores_gemma":[0.9996607,0.0001247639,0.00008173415,0.00002829434,0.00004840764,0.0000560686],"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":[0.00007940089,0.00006418978,0.008719168,0.001887815,0.0001302912,0.001296132,0.019694,0.002272247,0.002080447,0.7711639,0.04615277,0.1464597],"study_design_scores_gemma":[0.00001095495,0.0000635247,0.02007573,0.002541841,0.00005598464,0.0004432195,0.01669868,0.0003263941,0.000543979,0.1015317,0.8576789,0.00002911322],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1734318,0.4062047,0.002479326,0.1485027,0.0064795,0.00002514129,0.00030348,0.0001041854,0.2624693],"genre_scores_gemma":[0.849081,0.1365177,0.0003896794,0.006070345,0.000883523,0.00001931624,0.00008642139,0.00001476448,0.00693721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003089363,"threshold_uncertainty_score":0.01257938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1111057020770426,"score_gpt":0.4014126816725321,"score_spread":0.2903069795954895,"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."}}