{"id":"W4384935649","doi":"10.1175/jtech-d-22-0106.1","title":"Diagnosing Seasonal Forecast Skill of the Indian Ocean Dipole Mode Using Model Analogs","year":2023,"lang":"en","type":"article","venue":"Journal of Atmospheric and Oceanic Technology","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"Fundamental Research Funds for the Central Universities; Hohai University; National Natural Science Foundation of China","keywords":"Hindcast; Forecast skill; Climatology; Predictability; Forcing (mathematics); Coupled model intercomparison project; Anomaly (physics); Mode (computer interface); Data assimilation; Environmental science; Indian Ocean Dipole; Sea surface temperature; Meteorology; Computer science; Geology; Oceanography; Climate model; Statistics; Geography; Mathematics; Climate change; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002680394,0.0001801272,0.0003835753,0.00002700741,0.0002287843,0.00002613937,0.0004827457,0.0001846875,0.0000536381],"category_scores_gemma":[0.0001255816,0.0001172357,0.0001403941,0.001824379,0.0004420636,0.0002446027,0.00006783391,0.0003552602,0.000002298189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009180227,"about_ca_system_score_gemma":0.000224051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005578815,"about_ca_topic_score_gemma":0.00003271203,"domain_scores_codex":[0.9986292,0.00003213706,0.0004810364,0.0001827708,0.0003063382,0.0003685917],"domain_scores_gemma":[0.9989418,0.0001051103,0.0005470333,0.0001833843,0.0001164078,0.0001062627],"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":[0.00003467994,0.00003227783,0.9196109,0.00005645125,0.00008965637,0.00004410754,0.0003466858,0.0448245,0.00005726509,0.0002773722,0.0006292965,0.03399679],"study_design_scores_gemma":[0.0007391037,0.0003851763,0.2269417,0.0002451161,0.0001435372,0.0006529373,0.003560323,0.7415479,0.0003192196,0.02477287,0.0003798843,0.00031222],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951575,0.00240788,0.001442859,0.0005150356,0.0002183189,0.00007808378,0.00002098508,0.00004505829,0.000114246],"genre_scores_gemma":[0.9916215,0.0008903897,0.007261812,0.0001071849,0.00006064788,8.8524e-8,0.00000159429,0.000009008737,0.00004776407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6967234,"threshold_uncertainty_score":0.4780733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008961591146334635,"score_gpt":0.2189143033072381,"score_spread":0.2099527121609035,"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."}}