{"id":"W3013353379","doi":"10.1038/s41597-020-0446-2","title":"A global ensemble of ocean wave climate projections from CMIP5-driven models","year":2020,"lang":"en","type":"article","venue":"Scientific Data","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Lawrence Berkeley National Laboratory; Biological and Environmental Research; Japan Society for the Promotion of Science; Office of Science; Ministry of Education, Culture, Sports, Science and Technology; U.S. Department of Energy; Commonwealth Scientific and Industrial Research Organisation; National Energy Research Scientific Computing Center","keywords":"Downscaling; Climatology; Environmental science; Coupled model intercomparison project; Metadata; Climate model; Climate change; Significant wave height; Wind wave model; Wave model; Scale (ratio); Meteorology; Wind wave; Wind speed; Computer science; Geography; Geology; Cartography; Precipitation","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":[0.0009575849,0.0009722373,0.0006667649,0.0010553,0.0003252156,0.0008261124,0.0014507,0.0008113837,0.004161688],"category_scores_gemma":[0.001820223,0.0004106647,0.0009483086,0.003348711,0.0001936143,0.000941377,0.0007193821,0.001148531,0.002436244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006649611,"about_ca_system_score_gemma":0.00153187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03288445,"about_ca_topic_score_gemma":0.02908657,"domain_scores_codex":[0.9996833,0.00005149806,0.000030295,0.0001107488,0.00008085951,0.00004331363],"domain_scores_gemma":[0.9991198,0.0001406995,0.00008154025,0.0002416432,0.0003289571,0.00008729249],"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":[0.0005087579,0.0002832582,0.061186,0.0005910256,0.001325881,0.0003049691,0.0001457142,0.6202612,0.003246864,0.004064681,0.2536635,0.05441829],"study_design_scores_gemma":[0.0007028191,0.0001629498,0.1492861,0.0002365065,0.0003736266,0.0001581805,0.0002413447,0.6698828,0.006516356,0.004849406,0.1673009,0.0002890896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1716333,0.0003006076,0.009614707,0.0003874664,0.0002329982,0.0001086483,0.8104036,0.002260942,0.005057746],"genre_scores_gemma":[0.1346206,0.0001997013,0.01044078,0.00007750806,0.00004541826,0.000259093,0.8532917,0.0002325984,0.0008326261],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03288445,"threshold_uncertainty_score":0.06538606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043951185875507,"score_gpt":0.2496939400140287,"score_spread":0.145298821426478,"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."}}