{"id":"W2331994911","doi":"10.2514/6.2014-0913","title":"NSMB contribution to the 2nd High Lift Prediction Workshop","year":2014,"lang":"en","type":"article","venue":"52nd Aerospace Sciences Meeting","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Lift (data mining); Computer science; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.001265712,0.0001349909,0.0001208153,0.00005926151,0.0004774048,0.0002177904,0.0003016519,0.00005860771,0.000014881],"category_scores_gemma":[0.0003045167,0.00009606573,0.00004123173,0.0005478254,0.00007966607,0.0001640101,0.00006145285,0.0001423181,0.00006388544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000050198,"about_ca_system_score_gemma":0.00001269698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004527282,"about_ca_topic_score_gemma":0.0001276046,"domain_scores_codex":[0.9988056,0.00004132198,0.0001777772,0.0002496236,0.0003290006,0.0003966303],"domain_scores_gemma":[0.9994772,0.00014389,0.00003456615,0.0001984531,0.00005052385,0.00009536759],"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":[0.000002478849,0.000006374839,0.001809623,0.000007840962,0.000006909751,2.955773e-7,0.0002572069,0.9814023,0.004581111,0.005900214,0.003363233,0.002662411],"study_design_scores_gemma":[0.0001423347,0.00004956167,0.003906148,0.00008206686,0.000008824672,0.000001931994,0.00007612682,0.9717582,0.000913682,0.0002162427,0.02269211,0.0001527143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608629,0.0002370006,0.0276863,0.003268125,0.002202667,0.0002755849,0.00001033255,0.000339148,0.005117982],"genre_scores_gemma":[0.9978458,0.00002872191,0.001280668,0.0001714475,0.0003970162,0.00001950224,0.000005107736,0.00001310149,0.0002386835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03698289,"threshold_uncertainty_score":0.3917447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006150070529987212,"score_gpt":0.2017736075493892,"score_spread":0.195623537019402,"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."}}