{"id":"W4237682015","doi":"10.32920/ryerson.14648736","title":"A Mathematical Method for Predicting the Design Performance of Single and Multi-stage Rockets","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Rocket and propulsion systems research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Integrator; Range (aeronautics); Set (abstract data type); Computer science; Ordinary differential equation; Tandem; Baseline (sea); Nonlinear system; Path (computing); Multi stage; Aerospace engineering; Rocket (weapon); Mathematical optimization; Simulation; Control theory (sociology); Mathematics; Industrial engineering; Differential equation; Engineering; Mathematical analysis; Control (management); Artificial intelligence; Physics; Telecommunications","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":[0.0009314917,0.0009209698,0.0006260077,0.000961653,0.0004105399,0.0008252005,0.001269315,0.0010493,0.004510058],"category_scores_gemma":[0.003276906,0.0004910319,0.001018513,0.0007854323,0.0007034457,0.001312594,0.0007297485,0.001399994,0.001491991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000692161,"about_ca_system_score_gemma":0.001240907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002921687,"about_ca_topic_score_gemma":0.002007822,"domain_scores_codex":[0.9995651,0.00007501203,0.00001992576,0.00006910472,0.0002380438,0.00003281733],"domain_scores_gemma":[0.9991067,0.0004880174,0.00009492661,0.00007828163,0.0002076376,0.00002447052],"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":[0.00001446429,0.0000254215,0.0003775207,0.0001061583,0.00001515321,0.00003962084,0.00003757409,0.9313037,0.005189722,0.02824858,0.001202634,0.03343947],"study_design_scores_gemma":[0.000003065411,0.00001245302,0.00007151032,0.000009207567,0.000004479154,0.00001315422,0.000004043728,0.9930178,0.0009651911,0.004145653,0.001747553,0.000005969839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002165239,0.0001267002,0.9943135,0.00004762221,0.00003508927,0.00002572115,0.00005773124,0.0001921519,0.003036307],"genre_scores_gemma":[0.2757792,0.001263518,0.694905,0.0001419819,0.0001388817,0.0005928274,0.0004229272,0.0005780551,0.02617765],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004510058,"threshold_uncertainty_score":0.0150876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1617136357002388,"score_gpt":0.3602922802790922,"score_spread":0.1985786445788534,"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."}}