{"id":"W3105368355","doi":"10.2514/1.g005360","title":"Optimal Powered Aerogravity-Assist Trajectories","year":2020,"lang":"en","type":"article","venue":"Journal of Guidance Control and Dynamics","topic":"Spacecraft Dynamics and Control","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs","keywords":"Computer science; Trajectory optimization; Control theory (sociology); Aeronautics; Aerospace engineering; Optimal control; Mathematical optimization; Engineering; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002765268,0.0006980511,0.0003946529,0.0004641048,0.000399325,0.0007982779,0.0003450433,0.0009478691,0.006081444],"category_scores_gemma":[0.001630372,0.0002559825,0.0002345255,0.0002732216,0.0005015133,0.0006644758,0.001384009,0.0005117174,0.0007760563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003671952,"about_ca_system_score_gemma":0.0006709443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467459,"about_ca_topic_score_gemma":0.00143926,"domain_scores_codex":[0.9998939,0.00002710268,0.000004485227,0.00001277133,0.00003469204,0.0000269755],"domain_scores_gemma":[0.9996375,0.000144826,0.00004985046,0.00003371436,0.00009105126,0.00004308219],"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":[0.0008829907,0.0001178263,0.001413224,0.0001938127,0.0000448444,0.0003435103,0.0002488911,0.8433448,0.01520494,0.06964087,0.00431858,0.06424575],"study_design_scores_gemma":[0.00007668119,0.0003582605,0.001013781,0.00005199287,0.00001182739,0.0001002892,0.0001140258,0.968227,0.00333937,0.02426055,0.002426853,0.00001949469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3608486,0.0004424692,0.5484803,0.001321695,0.0001854303,0.0002727903,0.0004792073,0.0007396465,0.08722986],"genre_scores_gemma":[0.9710253,0.0001382075,0.01681932,0.00007348342,0.00001705359,0.00009448134,0.0001025673,0.00005493454,0.01167462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006081444,"threshold_uncertainty_score":0.02034444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00415392014287825,"score_gpt":0.1875013248010556,"score_spread":0.1833474046581774,"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."}}