{"id":"W2532086395","doi":"10.1175/bams-d-16-0019.1","title":"PDRMIP: A Precipitation Driver and Response Model Intercomparison Project—Protocol and Preliminary Results","year":2016,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Climate variability and models","field":"Environmental Science","cited_by":210,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Pacific Institute for Climate Solutions","funders":"National Institute for Environmental Studies; Japan Society for the Promotion of Science; National Aeronautics and Space Administration; Department for Environment, Food and Rural Affairs, UK Government; Norges Forskningsråd; Sight Research UK; Natural Environment Research Council; Met Office","keywords":"Precipitation; Coupled model intercomparison project; Environmental science; Climatology; Climate change; Climate model; Forcing (mathematics); Range (aeronautics); Atmospheric sciences; Meteorology; Geology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02506582,0.001737003,0.0009221344,0.00105475,0.002571823,0.001779392,0.004879079,0.001698786,0.0402675],"category_scores_gemma":[0.02365997,0.001509421,0.001153007,0.001536201,0.001004689,0.001458385,0.004303217,0.002917278,0.008899949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002396943,"about_ca_system_score_gemma":0.008663265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01408591,"about_ca_topic_score_gemma":0.0102488,"domain_scores_codex":[0.9928784,0.004218565,0.0007478634,0.0006605936,0.000945925,0.0005486364],"domain_scores_gemma":[0.9897764,0.00254235,0.0007399474,0.003327206,0.0027255,0.0008886836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.03205528,0.007055692,0.03790952,0.00653536,0.001497283,0.00100864,0.002281835,0.1470441,0.03837618,0.0260233,0.5535128,0.1466999],"study_design_scores_gemma":[0.03563842,0.007981968,0.08007859,0.001887012,0.00102155,0.0003519189,0.001799978,0.1102841,0.05626405,0.02358335,0.6802403,0.0008687458],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"protocol","genre_scores_codex":[0.07332671,0.0002991918,0.1401226,0.002210947,0.0006080117,0.2560507,0.4912823,0.008434257,0.0276654],"genre_scores_gemma":[0.05012451,0.0002414093,0.143372,0.0006648916,0.0001393444,0.553737,0.2458891,0.001323964,0.004507845],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.0402675,"threshold_uncertainty_score":0.1347082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02432482170045183,"score_gpt":0.2796423023672091,"score_spread":0.2553174806667573,"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."}}