{"id":"W2123489470","doi":"10.1175/jcli-d-14-00691.1","title":"Designing Detection and Attribution Simulations for CMIP6 to Optimize the Estimation of Greenhouse Gas–Induced Warming","year":2015,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Environment and Climate Change Canada","funders":"","keywords":"Greenhouse gas; Environmental science; Climatology; Forcing (mathematics); Climate model; Transient climate simulation; Climate change; Monte Carlo method; Atmospheric sciences; Coupled model intercomparison project; Radiative forcing; Meteorology; Aerosol; Statistics; Mathematics; Geology","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":[0.006688361,0.0006767426,0.0005545993,0.0004179978,0.0005131699,0.000475872,0.0009947565,0.0007891663,0.001505457],"category_scores_gemma":[0.01696637,0.0006534402,0.000555238,0.000359448,0.0008374654,0.0008278342,0.0008462294,0.0009826912,0.0001078558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190109,"about_ca_system_score_gemma":0.001225144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003071828,"about_ca_topic_score_gemma":0.002308928,"domain_scores_codex":[0.9986601,0.0008882515,0.00005346675,0.0002020776,0.00007486839,0.0001211991],"domain_scores_gemma":[0.9888062,0.008094531,0.001020921,0.000803743,0.0008038128,0.0004707723],"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":[0.0006249961,0.0002238527,0.005530796,0.00004316934,0.00005662614,0.00001921479,0.00002804478,0.9834236,0.002632881,0.001452482,0.0001442508,0.005820096],"study_design_scores_gemma":[0.0001425517,0.000231909,0.001374158,0.000005212152,0.00002339516,0.000003670664,0.00001644439,0.9937225,0.003420331,0.0008858842,0.0001637504,0.00001022267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.918597,0.00008165869,0.07872935,0.0002149242,0.00004730714,0.0003221174,0.0002209643,0.0002429351,0.0015437],"genre_scores_gemma":[0.97333,0.00001559422,0.02616139,0.00002645407,0.00000687288,0.0002325463,0.0001062549,0.00001550943,0.0001053065],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006688361,"threshold_uncertainty_score":0.03537184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06619099262331239,"score_gpt":0.3068745598170028,"score_spread":0.2406835671936904,"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."}}