{"id":"W3034695376","doi":"10.1007/s00382-020-05322-2","title":"Projected future changes in rainfall in Southeast Asia based on CORDEX–SEA multi-model simulations","year":2020,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Philippine Council for Industry, Energy, and Emerging Technology Research and Development; Japan Society for the Promotion of Science; Centre for Asia-Pacific Initiatives; Russian Science Foundation; National Research Council of Thailand; Universiti Kebangsaan Malaysia; Asia-Pacific Network for Global Change Research; Ministry of Higher Education, Malaysia; Thailand Research Fund; Department of Science and Technology, Ministry of Science and Technology, India; National Foundation for Science and Technology Development","keywords":"Downscaling; Climatology; Precipitation; Climate model; Representative Concentration Pathways; General Circulation Model; Environmental science; Climate change; Period (music); Southeast asia; Geography; Geology; Meteorology; Oceanography; History","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004752349,0.0007570808,0.0003658283,0.0003994257,0.0002610442,0.0005248407,0.0005212059,0.0004264041,0.001342509],"category_scores_gemma":[0.0007263044,0.0002177308,0.0006534221,0.0005661451,0.0001649998,0.0004056457,0.0003178362,0.0003186326,0.0001417909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128749,"about_ca_system_score_gemma":0.0009180366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05681692,"about_ca_topic_score_gemma":0.03977028,"domain_scores_codex":[0.999896,0.00003135744,0.000008483436,0.00002598845,0.000017407,0.00002073759],"domain_scores_gemma":[0.9997436,0.0000517499,0.00003510413,0.00002504811,0.0001056607,0.00003872507],"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":[0.0002118525,0.00006481849,0.07194985,0.00005126791,0.0001139378,0.0002299817,0.00004419883,0.9195625,0.001618934,0.0003220085,0.0008104123,0.005020214],"study_design_scores_gemma":[0.000109537,0.0001359224,0.05371557,0.00001979802,0.00008806092,0.00004042804,0.0000801284,0.9421073,0.002085801,0.0002267389,0.00136072,0.00003005111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955148,0.00007943621,0.0009300156,0.00009567892,0.00001815823,0.00001540402,0.001666084,0.00008837115,0.001592015],"genre_scores_gemma":[0.9955227,0.0001059609,0.001306809,0.00002918928,0.000006808061,0.00003512616,0.002481379,0.00001480361,0.0004970988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05681692,"threshold_uncertainty_score":0.1129724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02866404830310496,"score_gpt":0.2612986982411656,"score_spread":0.2326346499380607,"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."}}