{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001477465,0.0002744836,0.0003376885,0.0000138369,0.00009478776,0.00004089241,0.0003794284,0.0006213399,0.0005247217],"category_scores_gemma":[0.0001767151,0.0002427379,0.0001088746,0.0003712414,0.000191191,0.0005231003,0.0005015747,0.0004931486,0.00007970397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006530004,"about_ca_system_score_gemma":0.00001694983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003987308,"about_ca_topic_score_gemma":0.0002507346,"domain_scores_codex":[0.9976766,0.000174778,0.0004109124,0.0005918477,0.0005893399,0.0005565743],"domain_scores_gemma":[0.9989073,0.00007625751,0.0001964469,0.000495987,0.00006778109,0.0002562092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004719523,0.0006193538,0.9475015,0.0001498507,0.0000234264,0.000007518682,0.001119485,0.001497033,0.03806808,0.00252808,0.006927783,0.001085946],"study_design_scores_gemma":[0.007823274,0.001247218,0.8466146,0.0004917122,0.0003071997,0.0001401631,0.001309977,0.08064642,0.01485932,0.0248216,0.01849835,0.003240193],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889954,0.000180244,0.00005031487,0.0006906235,0.0006404719,0.0004510935,0.001218952,0.0000903087,0.007682586],"genre_scores_gemma":[0.9981727,0.0001187889,0.0009591663,0.0004215585,0.0001127978,0.00001546542,0.000161348,0.00001848489,0.00001970568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1008869,"threshold_uncertainty_score":0.9898566,"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."}}