{"id":"W2964050632","doi":"10.1109/icra.2017.7989607","title":"Deep neural networks for improved, impromptu trajectory tracking of quadrotors","year":2017,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dynamic Systems Analysis (Canada)","funders":"","keywords":"Trajectory; Computer science; Controller (irrigation); Tracking (education); Control theory (sociology); Artificial neural network; PID controller; Nonlinear system; Artificial intelligence; Control engineering; Control (management); 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":[0.0001722006,0.0001563337,0.0002245901,0.00004555425,0.0003297376,0.0001848905,0.0007773261,0.00007817049,0.0000357492],"category_scores_gemma":[0.0002314538,0.0001242702,0.0001581371,0.00003319119,0.0001730494,0.0003719646,0.00008187473,0.0001357858,0.00000172197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001175893,"about_ca_system_score_gemma":0.00001163504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005281454,"about_ca_topic_score_gemma":0.0000627315,"domain_scores_codex":[0.9988612,0.00003417111,0.0002742951,0.0003870509,0.00009899064,0.0003442874],"domain_scores_gemma":[0.9988422,0.0002863733,0.0002527419,0.0005014375,0.00004516988,0.00007208461],"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":[0.0001555167,0.000146923,0.001152273,0.00007839012,0.00001581248,0.000004816405,0.0004404843,0.002467792,0.8351996,0.001294535,0.0004538145,0.15859],"study_design_scores_gemma":[0.0004565757,0.0002369137,0.001809109,0.0000103082,0.000007312028,0.000008072695,0.00003290328,0.6335051,0.363374,0.0001149181,0.000295332,0.000149416],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8703328,0.00005548706,0.1240392,0.0003955503,0.001797229,0.000880504,0.000009512047,0.0001298948,0.002359824],"genre_scores_gemma":[0.9979095,0.000002652278,0.001138592,0.0003556506,0.0002310971,0.00003005199,6.849164e-7,0.00002110454,0.0003106349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6310374,"threshold_uncertainty_score":0.5067593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0421423574292548,"score_gpt":0.2994822501827175,"score_spread":0.2573398927534626,"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."}}