{"id":"W2313294087","doi":"10.2514/6.2016-1912","title":"Multi-parametric high-order flow sensitivity analysis","year":2016,"lang":"en","type":"article","venue":"57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Sensitivity (control systems); Computer science; Parametric statistics; Flow (mathematics); Mathematics; Engineering; Electronic engineering; Statistics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002562731,0.0008282756,0.001232121,0.0004346168,0.00044615,0.0004631375,0.0003693616,0.0002921809,0.00338471],"category_scores_gemma":[0.00005463805,0.0005804732,0.0002660069,0.0007147742,0.00044458,0.0004773725,0.0002748992,0.0003181105,0.00002333474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009764791,"about_ca_system_score_gemma":0.000108652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001963501,"about_ca_topic_score_gemma":0.0005018272,"domain_scores_codex":[0.9961913,0.0003584216,0.0008238202,0.001249499,0.0004321556,0.0009448256],"domain_scores_gemma":[0.9977418,0.000169264,0.0004964872,0.0007837379,0.0003807031,0.0004279752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007611616,0.0001358618,0.02171229,0.0002391056,0.004111866,0.00006446832,0.0005656499,0.003845277,0.1001868,0.2836943,0.0006846176,0.5839987],"study_design_scores_gemma":[0.006966872,0.0002688431,0.6482775,0.0001684877,0.002171199,0.0001088933,0.0003470055,0.2399624,0.03166456,0.06519108,0.000623659,0.004249531],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093074,0.0000630435,0.08528766,0.0003412217,0.002041248,0.0004792702,0.002175781,0.0001543955,0.0001499509],"genre_scores_gemma":[0.9895848,0.0001003153,0.007816146,0.0001111336,0.0007077669,0.00002653219,0.0006601978,0.00005942411,0.0009336944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6265652,"threshold_uncertainty_score":0.9996647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177602236830337,"score_gpt":0.2378981628182924,"score_spread":0.226122140449989,"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."}}