{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002204015,0.0002042552,0.0002237056,0.00008228424,0.0000791334,0.00002886141,0.0002223637,0.0001582735,0.0002259428],"category_scores_gemma":[0.00008301095,0.0002029685,0.00004566992,0.0005321215,0.00008691377,0.0001078028,0.0001449881,0.000291216,0.00005907976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003360143,"about_ca_system_score_gemma":0.00002518054,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001379319,"about_ca_topic_score_gemma":0.01833937,"domain_scores_codex":[0.9985365,0.00007890449,0.0002776575,0.0004741812,0.0002067615,0.000426036],"domain_scores_gemma":[0.9994525,0.00007568498,0.00007288017,0.0002734383,0.000009330196,0.0001162076],"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":[0.0001015424,0.000281776,0.102714,0.00004159737,0.000001326373,0.000006358011,0.0009995374,0.8945277,0.0005620719,0.0004065791,0.00001024347,0.0003472098],"study_design_scores_gemma":[0.001028043,0.00006348897,0.0286296,0.00002652814,0.000005830194,3.04707e-7,0.0006495379,0.9692144,0.000005296498,0.0001019398,0.00006144913,0.0002135613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833713,0.000003253235,0.003962363,0.006384369,0.00008497692,0.000925392,0.0009986988,0.0001282544,0.00414136],"genre_scores_gemma":[0.9959155,0.00001590889,0.002929976,0.0006974105,0.00002297035,0.00003568548,0.0003392386,0.00002854978,0.00001471891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0746867,"threshold_uncertainty_score":0.9995733,"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."}}