{"id":"W2884458754","doi":"10.2514/6.2018-3647.c1","title":"Correction: Application of Leading Edge Tubercles to Enhance Propeller Performance","year":2018,"lang":"en","type":"article","venue":"2018 Applied Aerodynamics Conference","topic":"Aerosol Filtration and Electrostatic Precipitation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Propeller; Computer science; Enhanced Data Rates for GSM Evolution; Marine engineering; Artificial intelligence; Engineering","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":[0.0001378645,0.0001591783,0.0001846533,0.00007393808,0.0000916168,0.00003132926,0.0001862472,0.00007811048,0.00005542522],"category_scores_gemma":[0.00001643525,0.0001706727,0.00001975923,0.0003117285,0.00009230506,0.0001286649,0.00002552139,0.0001261321,0.0005399255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007723634,"about_ca_system_score_gemma":0.00003695695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007232316,"about_ca_topic_score_gemma":0.0001338647,"domain_scores_codex":[0.9990512,0.000009583475,0.0003008519,0.0002398419,0.0001624365,0.0002360636],"domain_scores_gemma":[0.9993772,0.00003254942,0.00006684761,0.0002774346,0.0001735745,0.00007241654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005790791,0.00003410147,0.0002485235,0.00009701484,0.00002454785,6.757976e-8,0.00212739,0.007351559,0.815383,0.008019961,0.004083028,0.1625729],"study_design_scores_gemma":[0.0001262654,0.0001377376,0.003165185,0.00004972744,0.00001203859,0.000002317199,0.000113666,0.6821306,0.3118952,0.0002466,0.001805254,0.0003153525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4213817,0.000005960816,0.5368948,0.00003104369,0.00049073,0.0003472019,0.000004617649,0.000173916,0.04067008],"genre_scores_gemma":[0.9952546,0.00002784094,0.003721718,0.00005906531,0.0001155219,0.0001074171,0.00003285046,0.00002712351,0.0006538728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6747791,"threshold_uncertainty_score":0.6959832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008521132166963019,"score_gpt":0.233298982887263,"score_spread":0.2247778507203,"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."}}