{"id":"W2625956171","doi":"10.1175/mwr-d-16-0337.1","title":"A Postprocessing Method for Seasonal Forecasts Using Temporally and Spatially Smoothed Statistics","year":2017,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Climate variability and models","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Statistics; Environmental science; Climatology; Meteorology; Computer science; Mathematics; Geology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009705741,0.0001629795,0.0003229785,0.000008578751,0.0004202949,0.0001203658,0.0002389184,0.00004911963,0.0003629728],"category_scores_gemma":[0.0003438144,0.0001342771,0.00006463108,0.00002789357,0.0001224563,0.0002500471,0.00017981,0.00006190025,0.000009700723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006120894,"about_ca_system_score_gemma":0.00003265208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004678356,"about_ca_topic_score_gemma":0.0004204656,"domain_scores_codex":[0.9989169,0.00006662074,0.0002581714,0.0003482853,0.0001731803,0.0002368393],"domain_scores_gemma":[0.9991148,0.0001144442,0.0002478929,0.0004023465,0.00002084885,0.00009968276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001047823,0.000217839,0.02860592,0.006379271,0.00006750508,0.0000173126,0.0007357646,0.0008491854,0.003016917,0.001067895,0.001557957,0.9573796],"study_design_scores_gemma":[0.001945787,0.0003588269,0.01962249,0.007597892,0.0007737318,0.00004033647,0.00002581829,0.8008399,0.0001855801,0.03344966,0.133816,0.001343999],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1176701,0.03623245,0.8228497,0.004397947,0.0002963608,0.005099535,0.001109918,0.0001130148,0.01223097],"genre_scores_gemma":[0.05096376,0.003596352,0.9431183,0.001689595,0.00006929093,0.0001015383,0.00003200004,0.0000643352,0.0003648319],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9560357,"threshold_uncertainty_score":0.547566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06122710430577642,"score_gpt":0.3502976399433846,"score_spread":0.2890705356376082,"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."}}