{"id":"W2898406280","doi":"10.1155/2018/4720523","title":"Assessment of Climate Change Impacts on Extreme Precipitation Events: Applications of CMIP5 Climate Projections Statistically Downscaled over South Korea","year":2018,"lang":"en","type":"article","venue":"Advances in Meteorology","topic":"Climate variability and models","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas","funders":"Korea Agency for Infrastructure Technology Advancement; Ministry of Land, Infrastructure and Transport","keywords":"Precipitation; Climate change; Climatology; Representative Concentration Pathways; Environmental science; Climate model; Return period; Scale (ratio); Period (music); Geography; Meteorology; Cartography; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0006896456,0.0001420158,0.0002908414,0.0001139143,0.00008099107,0.000003623601,0.0001792231,0.00009315542,0.0006832714],"category_scores_gemma":[0.0001096668,0.0001278438,0.00004870623,0.000381192,0.0004404117,0.0003201133,0.0001353331,0.0001182896,0.00003032706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001261603,"about_ca_system_score_gemma":0.00001391234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001285374,"about_ca_topic_score_gemma":0.001204157,"domain_scores_codex":[0.9983068,0.0001902927,0.0005172021,0.0003858906,0.0002337049,0.0003661137],"domain_scores_gemma":[0.9989157,0.0003201729,0.000313681,0.0003554686,0.00003689742,0.00005809949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005528467,0.001842442,0.849606,0.000326315,0.00003837444,0.00000159814,0.003257495,0.004292582,0.02088651,0.06348196,0.00001298246,0.05570088],"study_design_scores_gemma":[0.001205917,0.001691277,0.9546469,0.00007159232,0.00006710116,0.000002843127,0.0002079197,0.01107534,0.0008035434,0.0290847,0.0008869132,0.0002559476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500344,0.00005335503,0.03109605,0.0001172915,0.0002048788,0.001557517,0.0004888379,0.00003761124,0.01640999],"genre_scores_gemma":[0.9791576,0.0007566864,0.01944892,0.0001108145,0.00003364838,0.0004393818,0.00003609095,0.00001187892,0.000005028644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1050409,"threshold_uncertainty_score":0.7481343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673350416593851,"score_gpt":0.3365552298728417,"score_spread":0.3098217257069032,"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."}}