{"id":"W2913929460","doi":"10.1109/icuas.2019.8798185","title":"Design and Shape Optimization of Unmanned, Semi-Rigid Airship for Rapid Descent Using Hybrid Genetic Algorithm","year":2019,"lang":"en","type":"article","venue":"","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Descent (aeronautics); Payload (computing); Genetic algorithm; Aerospace engineering; Computer science; Keel; Envelope (radar); Hull; Marine engineering; Simulation; Algorithm; Engineering; Structural 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003510018,0.0005274576,0.0003902097,0.0005348297,0.0002165802,0.0005072172,0.0004800077,0.0006054008,0.0008864648],"category_scores_gemma":[0.0005160865,0.0002804379,0.0005374054,0.0003368415,0.0003623591,0.0002127414,0.0004033802,0.0002663783,0.0001566393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003170907,"about_ca_system_score_gemma":0.0007077212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002815127,"about_ca_topic_score_gemma":0.002551096,"domain_scores_codex":[0.9998883,0.00003212078,0.000004320367,0.0000197621,0.00003679235,0.00001869943],"domain_scores_gemma":[0.999863,0.00005897453,0.00003012354,0.000008977267,0.00002889191,0.0000100129],"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":[0.00001472583,0.00001814195,0.0002861743,0.00001994378,0.00001257507,0.0000272504,0.00001675918,0.9868675,0.002695097,0.0008024996,0.00009894268,0.009140415],"study_design_scores_gemma":[0.000005202247,0.00003850005,0.0001196721,0.000002487167,0.00000414749,0.000006213847,0.000008003254,0.9990137,0.0003972681,0.0001945731,0.0002081843,0.000002031965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1735693,0.0003773486,0.8170192,0.0001147825,0.00003870511,0.0001293884,0.00004834763,0.0002772022,0.00842573],"genre_scores_gemma":[0.8184384,0.000230521,0.1782711,0.00005445561,0.000009300811,0.000245389,0.00009650938,0.00004019213,0.002614171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002815127,"threshold_uncertainty_score":0.005597472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411831560483756,"score_gpt":0.1904888818906809,"score_spread":0.1763705662858433,"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."}}