{"id":"W4220676084","doi":"10.3389/fclim.2022.836817","title":"Verification Data and the Skill of Decadal Predictions","year":2022,"lang":"en","type":"article","venue":"Frontiers in Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Environment and Climate Change Canada","funders":"Japan Society for the Promotion of Science","keywords":"Forecast skill; Ensemble average; Forecast verification; Ensemble forecasting; Computer science; Statistics; Climatology; Econometrics; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01864505,0.000453431,0.0005223433,0.001746404,0.000682461,0.002397227,0.0009721841,0.000608251,0.003592812],"category_scores_gemma":[0.1043184,0.0003181803,0.0006086709,0.00185567,0.0006995715,0.003009976,0.002283311,0.001440638,0.001012881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009367655,"about_ca_system_score_gemma":0.001398361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009308505,"about_ca_topic_score_gemma":0.006743552,"domain_scores_codex":[0.9942145,0.00195951,0.0007254025,0.001105403,0.001682292,0.0003128913],"domain_scores_gemma":[0.8784596,0.07504148,0.01007271,0.01720555,0.01792422,0.001296462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00164866,0.0002149056,0.5854252,0.0004548258,0.000806931,0.0002758632,0.002021318,0.1329773,0.005637452,0.02613999,0.01199462,0.232403],"study_design_scores_gemma":[0.0001528738,0.0003002488,0.6517561,0.0003796878,0.0001962691,0.0002251978,0.001534779,0.2571416,0.01338769,0.04641877,0.02828613,0.0002206404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8115738,0.001006842,0.1280379,0.001501469,0.0004933645,0.0002231605,0.01788124,0.001861556,0.03742049],"genre_scores_gemma":[0.9772955,0.0001405427,0.0118412,0.0001269738,0.00004881611,0.00007897805,0.009046157,0.0002131537,0.001208584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01864505,"threshold_uncertainty_score":0.09860563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889011530484971,"score_gpt":0.2465698503166032,"score_spread":0.2276797350117535,"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."}}