{"id":"W2026320710","doi":"10.1038/nclimate2605","title":"Decadal modulation of global surface temperature by internal climate variability","year":2015,"lang":"en","type":"article","venue":"Nature Climate Change","topic":"Climate variability and models","field":"Environmental Science","cited_by":505,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Climatology; Environmental science; Global warming; Global temperature; Greenhouse gas; Volcano; Slowdown; Pacific decadal oscillation; Atmospheric sciences; Sea surface temperature; Climate change; Climate model; Mode (computer interface); Hiatus; Mean radiant temperature; Surface air temperature; Geology; Oceanography; Economics","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":[0.0003280102,0.0002873054,0.0002046955,0.0002121381,0.000195738,0.000895521,0.0002092848,0.000468558,0.001677688],"category_scores_gemma":[0.001010072,0.0002610723,0.0004219901,0.0003802412,0.000323599,0.0005933693,0.0004883868,0.0006057988,0.000265158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004372506,"about_ca_system_score_gemma":0.0002543748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004268984,"about_ca_topic_score_gemma":0.003517113,"domain_scores_codex":[0.9999471,0.00001503427,0.000003491312,0.00001741748,0.000005710524,0.00001134298],"domain_scores_gemma":[0.9997641,0.0000879856,0.00004344382,0.00004297726,0.00003010546,0.00003129344],"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":[0.0005121368,0.0001668516,0.2494265,0.0001255576,0.0005113006,0.0002161012,0.0003730064,0.6558813,0.03160284,0.02030272,0.006757126,0.03412459],"study_design_scores_gemma":[0.00005042093,0.00005488974,0.2482254,0.00002454409,0.0001249746,0.00009321053,0.0001180069,0.7340928,0.002550373,0.01118058,0.003437744,0.00004702541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858374,0.0004029381,0.00671497,0.0007909351,0.0001082964,0.000004340133,0.0007520729,0.0002011653,0.005187818],"genre_scores_gemma":[0.9987387,0.0001392888,0.0003262695,0.00004165152,0.00001774913,0.000002846066,0.0001895453,0.00004566801,0.0004984203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004268984,"threshold_uncertainty_score":0.008488238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02516961518955154,"score_gpt":0.2812079893213857,"score_spread":0.2560383741318342,"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."}}