{"id":"W3129742033","doi":"10.1175/jcli-d-20-0703.1","title":"Assessing Prior Emergent Constraints on Surface Albedo Feedback in CMIP6","year":2021,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Lawrence Livermore National Laboratory; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Climatology; Albedo (alchemy); Snow; Environmental science; Northern Hemisphere; Sea ice; Coupled model intercomparison project; Climate model; Outlier; Climate sensitivity; Climate change; Atmospheric sciences; Geology; Meteorology; Oceanography; Computer science; 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":[0.002090862,0.0005951021,0.0003196991,0.0005340732,0.0004636554,0.0006913613,0.0007437858,0.0008320533,0.002048333],"category_scores_gemma":[0.007398916,0.0003303523,0.0005305965,0.0005371161,0.0004368754,0.001188778,0.0007889328,0.0006989347,0.0001876795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011342,"about_ca_system_score_gemma":0.0006338197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02437051,"about_ca_topic_score_gemma":0.01511004,"domain_scores_codex":[0.9996581,0.00008999844,0.00002203965,0.0001025241,0.00006391845,0.000063466],"domain_scores_gemma":[0.9981312,0.0009347458,0.0002059152,0.0002291857,0.0003641334,0.0001347641],"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":[0.0004430723,0.00009607723,0.3999354,0.0002156626,0.0004202733,0.0003380687,0.0002429841,0.5685889,0.007741474,0.003439013,0.002894437,0.01564456],"study_design_scores_gemma":[0.00007805663,0.00006766993,0.2574367,0.00007216608,0.00006938641,0.00009613453,0.000151226,0.7329066,0.003462477,0.002791307,0.002797797,0.00007063782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913501,0.0001415631,0.003447023,0.0002762177,0.00002108842,0.00001525687,0.002880829,0.0002009835,0.00166698],"genre_scores_gemma":[0.9975187,0.00002929459,0.0007404407,0.00003044459,0.000009573624,0.000007497587,0.001580088,0.00003790149,0.00004613506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02437051,"threshold_uncertainty_score":0.04845732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03409292212120649,"score_gpt":0.3066090611146038,"score_spread":0.2725161389933973,"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."}}