{"id":"W2979407507","doi":"10.3390/drones3040077","title":"Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications","year":2019,"lang":"en","type":"article","venue":"Drones","topic":"Guidance and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Propeller; Thrust; Moment (physics); Series (stratigraphy); Parametric statistics; Wind tunnel; Torque; Taylor series; Parametric model; Oblique case; Computer science; Flow (mathematics); Multinomial distribution; Applied mathematics; Control theory (sociology); Mathematics; Engineering; Artificial intelligence; Physics; Aerospace engineering; Mathematical analysis; Marine engineering; Geometry; Geology; Classical mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000594504,0.00006176563,0.00009975054,0.00004073392,0.00002076065,0.0000140035,0.00004497684,0.00002342123,0.000001893747],"category_scores_gemma":[5.503067e-7,0.00005701904,0.00001940279,0.00004749756,0.000006175555,0.00003890186,0.000007500164,0.00002154998,0.00002017149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002906302,"about_ca_system_score_gemma":0.00000663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003926938,"about_ca_topic_score_gemma":0.000007362332,"domain_scores_codex":[0.9995952,0.000003200201,0.0001246856,0.0001020599,0.00006371074,0.00011119],"domain_scores_gemma":[0.9998465,0.00002270362,0.00001374998,0.00007624439,0.0000193113,0.00002152512],"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":[0.00000442133,0.00001059615,0.0001686699,0.0000992225,0.00001070953,8.29072e-8,0.0002617436,0.9871348,0.001165419,0.009462497,0.00007582457,0.001606027],"study_design_scores_gemma":[0.0004994767,0.00001414901,0.0004087487,0.00001974453,0.000003547932,7.304308e-7,0.00008497405,0.993296,0.0001154603,0.001885038,0.00359401,0.00007815319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3321156,0.0008728875,0.6604285,0.0002030293,0.0001136236,0.002061586,0.00001549738,0.00008625909,0.00410301],"genre_scores_gemma":[0.9982457,0.000007593922,0.0005854393,0.00001749677,0.00002955145,0.0005107324,0.00001065742,0.00001157367,0.0005812612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6661301,"threshold_uncertainty_score":0.2325169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004942338148185106,"score_gpt":0.1856611315444989,"score_spread":0.1807187933963138,"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."}}