{"id":"W4406370651","doi":"10.1007/s00162-025-00737-1","title":"Active learning of data-assimilation closures using graph neural networks","year":2025,"lang":"en","type":"article","venue":"Theoretical and Computational Fluid Dynamics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Safran Electronics (Canada)","funders":"Safran; Agence Nationale de la Recherche","keywords":"Computational Science and Engineering; Artificial neural network; Computer science; Data assimilation; Graph; Artificial intelligence; Machine learning; Theoretical computer science; Meteorology; 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":[0.0001009349,0.00009868971,0.0001462868,0.00005238825,0.0001317629,0.00002562102,0.0001017516,0.00003713072,0.0000621498],"category_scores_gemma":[0.000005572015,0.00008875575,0.00004114787,0.0001666407,0.0002521522,0.0001124913,0.0001137834,0.0001730369,2.276336e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009474564,"about_ca_system_score_gemma":0.00002147501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001199107,"about_ca_topic_score_gemma":3.880612e-7,"domain_scores_codex":[0.9993014,0.00008058544,0.0002000773,0.0001966468,0.0001041245,0.0001171173],"domain_scores_gemma":[0.9995217,0.0002107985,0.00005797408,0.00009578738,0.00007462803,0.00003909598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003840818,0.00001760945,0.001346892,0.000002889256,0.00002671855,8.983294e-8,0.000009875154,0.4591081,0.000007113866,0.5176789,0.00001125179,0.02175217],"study_design_scores_gemma":[0.0001690428,0.00001448897,0.001201848,0.0000120867,0.00003256661,7.476164e-7,0.00005336844,0.7727209,0.000006653062,0.2257244,0.000004661718,0.00005922036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3017265,0.00002806725,0.6967646,0.0001751231,0.00007907421,0.00005780791,0.00002470301,0.00001250919,0.001131636],"genre_scores_gemma":[0.9977515,0.000004356179,0.001670315,0.00004963841,0.0000813471,0.000001483021,0.0004082483,0.000006160803,0.00002693912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.696025,"threshold_uncertainty_score":0.3619354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132174352704748,"score_gpt":0.2811120564222224,"score_spread":0.2678946211517476,"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."}}