{"id":"W4221138619","doi":"10.1080/08839514.2022.2074129","title":"A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction","year":2022,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Solar and Space Plasma Dynamics","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; New York University","keywords":"Sunspot; Deep learning; Sunspot number; Artificial intelligence; Ensemble learning; Ensemble forecasting; Computer science; Meteorology; Machine learning; Solar cycle; Geography; Physics; Solar wind","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003916123,0.001534733,0.001389664,0.001207962,0.0003904341,0.0009218599,0.001278018,0.0009271901,0.000679636],"category_scores_gemma":[0.007054968,0.000314611,0.0009792056,0.001251375,0.0002887369,0.001647971,0.0008304181,0.001470741,0.0002167006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006924222,"about_ca_system_score_gemma":0.001319585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01623609,"about_ca_topic_score_gemma":0.01367848,"domain_scores_codex":[0.9990441,0.0003422892,0.00008167259,0.0001844959,0.0002451623,0.0001020999],"domain_scores_gemma":[0.9966781,0.002021886,0.0001643034,0.0003114243,0.0006914813,0.0001328143],"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":[0.0004370173,0.0002708512,0.01788975,0.0002500725,0.0006568714,0.00006774232,0.00008014476,0.7272962,0.0008756052,0.002086411,0.006168224,0.2439212],"study_design_scores_gemma":[0.00001190269,0.000074214,0.001737314,0.00002329262,0.00004893518,0.00001259956,0.00001838824,0.9960336,0.0003929539,0.001058476,0.0005784295,0.000009840026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5323012,0.04066306,0.4068164,0.003245818,0.001459748,0.000151128,0.001704333,0.003664199,0.009994132],"genre_scores_gemma":[0.9273599,0.004735707,0.06125874,0.0004928808,0.0003906837,0.00007650087,0.002727854,0.0001425625,0.002815061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01623609,"threshold_uncertainty_score":0.03228319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02941046650175375,"score_gpt":0.3501414002060442,"score_spread":0.3207309337042904,"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."}}