{"id":"W2320736119","doi":"10.2514/6.2013-1468","title":"Data Assimilation for Large-Scale Computational Models","year":2013,"lang":"en","type":"article","venue":"54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Innovation Trust","keywords":"Data assimilation; Computer science; Scale (ratio); Assimilation (phonology); Meteorology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003845665,0.0005471176,0.000719243,0.0001466936,0.0007349525,0.0007604065,0.0008535786,0.00032223,0.006095966],"category_scores_gemma":[0.0001274233,0.0004263776,0.00008312488,0.000150218,0.000261773,0.001305929,0.0001747889,0.0002323204,0.00003525952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002397811,"about_ca_system_score_gemma":0.0001218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061075,"about_ca_topic_score_gemma":0.00268585,"domain_scores_codex":[0.996549,0.0002020434,0.0008657743,0.001075388,0.0004765787,0.0008312925],"domain_scores_gemma":[0.9978581,0.0003644581,0.0003876113,0.0006860379,0.0003293576,0.0003744423],"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":[0.001305905,0.00009374401,0.04016753,0.001109796,0.0006738456,0.00001895384,0.002602907,0.1977464,0.002566707,0.5564943,0.00537365,0.1918462],"study_design_scores_gemma":[0.000608897,0.00008329994,0.2449033,0.00001094095,0.00003857584,0.00001155437,0.00008757644,0.4880849,0.00001855234,0.265633,0.0001729425,0.0003465612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9334008,0.0002026763,0.04247128,0.0005547219,0.001528558,0.001435905,0.01909256,0.000164007,0.001149543],"genre_scores_gemma":[0.9557674,0.00005699483,0.02337054,0.0004707687,0.0003457684,0.00001680138,0.01975536,0.00001967865,0.0001966701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2908613,"threshold_uncertainty_score":0.9998188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04616450687780187,"score_gpt":0.2575893336787212,"score_spread":0.2114248268009193,"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."}}