{"id":"W4404414732","doi":"10.1029/2024jh000243","title":"Using Explainable AI and Transfer Learning to Understand and Predict the Maintenance of Atlantic Blocking With Limited Observational Data","year":2024,"lang":"en","type":"article","venue":"Journal of Geophysical Research Machine Learning and Computation","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Climate Extremes; Schmidt Family Foundation; National Science Foundation","keywords":"Blocking (statistics); Observational study; Transfer of learning; Observational learning; Computer science; Artificial intelligence; Psychology; Mathematics education; Mathematics; Computer network; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009380191,0.000543392,0.0002609867,0.0006334081,0.0002489891,0.0004484786,0.0007383374,0.000608912,0.001053255],"category_scores_gemma":[0.00338476,0.0002412606,0.0004711708,0.000430823,0.0004977947,0.0009289861,0.0005829842,0.0008240003,0.0001398461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009752045,"about_ca_system_score_gemma":0.0007060835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03086893,"about_ca_topic_score_gemma":0.02414419,"domain_scores_codex":[0.9998652,0.00004043559,0.000008402953,0.00004301006,0.00001587864,0.00002721555],"domain_scores_gemma":[0.9986462,0.0008261344,0.0001639441,0.0001629307,0.0001360334,0.00006474596],"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":[0.0000592169,0.00008274721,0.02361766,0.00001676493,0.00004855897,0.00004578909,0.00003228592,0.9542795,0.0009128851,0.0006740977,0.00046713,0.01976329],"study_design_scores_gemma":[0.000001924637,0.000005032675,0.001289769,6.314168e-7,0.000001649486,0.000001148749,0.000002657776,0.9981377,0.0001240169,0.0004125573,0.0000214308,0.000001457102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.944779,0.0001267455,0.05271305,0.0004571731,0.00002136059,0.00002502975,0.0004152637,0.0004981135,0.0009640724],"genre_scores_gemma":[0.9956345,0.000017257,0.003674645,0.0000285006,0.00001159053,0.00001228658,0.0003539199,0.00001040343,0.0002568479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03086893,"threshold_uncertainty_score":0.06137848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09927608756865201,"score_gpt":0.3485107465980402,"score_spread":0.2492346590293882,"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."}}