{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001841425,0.0000595603,0.00008033048,0.00007630399,0.0001920051,0.00004448377,0.00009952928,0.00005314375,0.0008504595],"category_scores_gemma":[0.0006143977,0.00005646544,0.00001549803,0.0002917184,0.0002907666,0.0001998316,0.0002153618,0.0001630266,0.0001881808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001131163,"about_ca_system_score_gemma":0.00001121659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002029231,"about_ca_topic_score_gemma":0.0005821228,"domain_scores_codex":[0.9985455,0.0002718978,0.0001377537,0.0002733037,0.0003869475,0.0003845783],"domain_scores_gemma":[0.9984474,0.001253308,0.00001696059,0.0001631011,0.00001889384,0.0001003832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001287332,0.0004144971,0.8680826,0.00008304987,0.000004865199,0.000003495105,0.001720103,0.08713406,0.0130071,0.01087387,0.002046904,0.01650065],"study_design_scores_gemma":[0.0003761801,0.00007817263,0.4205937,0.00004129969,0.00000188578,0.000002520992,0.0001132198,0.5621781,0.00007872748,0.01181358,0.004617447,0.0001051277],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917933,0.00001567489,0.00004521333,0.001084724,0.00002022919,0.000180824,0.00001368261,0.00001569296,0.006830597],"genre_scores_gemma":[0.9992689,0.0001008566,0.0004068458,0.00005352254,0.00003067013,0.00001979585,0.0000279323,0.000006972219,0.00008450609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4750441,"threshold_uncertainty_score":0.9311935,"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."}}