{"id":"W4377193844","doi":"10.1007/s11430-022-1094-0","title":"A multi-model prediction system for ENSO","year":2023,"lang":"en","type":"article","venue":"Science China Earth Sciences","topic":"Climate variability and models","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology; National Key Research and Development Program of China; Nanjing University of Information Science and Technology; National Natural Science Foundation of China","keywords":"Predictability; Anomaly (physics); Probabilistic logic; El Niño Southern Oscillation; Forecast skill; Ensemble forecasting; Computer science; Mean squared prediction error; Mean squared error; Climatology; Econometrics; Meteorology; Environmental science; Machine learning; Statistics; Artificial intelligence; Mathematics; Geography; 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":[0.001034665,0.001020083,0.0007965155,0.0007007172,0.0008996307,0.001040089,0.001470585,0.0009018823,0.00549348],"category_scores_gemma":[0.002119686,0.000622855,0.000674607,0.0005688155,0.00020591,0.001825733,0.0008083739,0.001033454,0.001336521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009605345,"about_ca_system_score_gemma":0.001349189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02780814,"about_ca_topic_score_gemma":0.01836666,"domain_scores_codex":[0.9997637,0.00005426742,0.0000278279,0.00009182836,0.00004513753,0.000017306],"domain_scores_gemma":[0.9992287,0.00021615,0.00007116621,0.0001337172,0.0002661942,0.00008401999],"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":[0.0003688181,0.0002316891,0.006555351,0.0000646214,0.0001806568,0.0001307702,0.00007200574,0.928213,0.004672692,0.001749441,0.007805446,0.04995557],"study_design_scores_gemma":[0.00002061406,0.000009411272,0.0003527415,0.000001485984,0.00001210274,0.000002662664,0.000002794102,0.9984261,0.0003721231,0.000398347,0.0003945775,0.000007101277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2916172,0.0003296365,0.6312458,0.001031181,0.0007224068,0.0004410621,0.01287987,0.05364621,0.008086601],"genre_scores_gemma":[0.8199366,0.0001472276,0.1655983,0.0001263067,0.0001118607,0.0003614791,0.009551863,0.001170503,0.00299586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02780814,"threshold_uncertainty_score":0.05529255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04461625906473701,"score_gpt":0.2842810544518685,"score_spread":0.2396647953871315,"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."}}