{"id":"W2334309988","doi":"10.3354/cr01221","title":"Seasonal and regional biases in CMIP5 precipitation simulations","year":2014,"lang":"en","type":"article","venue":"Climate Research","topic":"Climate variability and models","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of International Science and Engineering; Lawrence Livermore National Laboratory; Bureau of Reclamation; U.S. Department of Energy; National Science Foundation","keywords":"Precipitation; Climatology; Cru; Coupled model intercomparison project; Environmental science; Climate model; Monsoon; Arid; Geography; Climate change; Meteorology; Geology; Oceanography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002727279,0.0005251727,0.0003395485,0.0005170213,0.0002674951,0.0008946732,0.000737436,0.0004829417,0.0007502568],"category_scores_gemma":[0.006837678,0.0002907141,0.000518358,0.0009854489,0.0001769694,0.0007241502,0.0004890045,0.0004030639,0.0002161134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120313,"about_ca_system_score_gemma":0.001063475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03670145,"about_ca_topic_score_gemma":0.02465642,"domain_scores_codex":[0.9992282,0.0002681121,0.00009109222,0.0001786543,0.0001368505,0.00009700184],"domain_scores_gemma":[0.9985808,0.0005212635,0.0002310522,0.0002261131,0.0003759465,0.00006482946],"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":[0.0002395075,0.00005549474,0.2609948,0.0001128847,0.0003313355,0.0001092853,0.0001605172,0.7134098,0.003169602,0.001693017,0.002339204,0.01738463],"study_design_scores_gemma":[0.00008691736,0.00005995591,0.1105111,0.0000911378,0.0001011473,0.00008833125,0.0001847626,0.8773051,0.005811013,0.001749074,0.003947895,0.00006367105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806753,0.0005473993,0.009228511,0.0003401694,0.00007334151,0.00003708684,0.004245987,0.0008271136,0.004025187],"genre_scores_gemma":[0.994283,0.0001331928,0.003326807,0.00006241913,0.00001157909,0.00001690773,0.001858577,0.000075294,0.0002321637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03670145,"threshold_uncertainty_score":0.07297564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1612897788371268,"score_gpt":0.3970908809004848,"score_spread":0.235801102063358,"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."}}