{"id":"W2541450990","doi":"","title":"Automization of an INS/GPS intecrated system using genetic optimization","year":2004,"lang":"en","type":"article","venue":"World Automation Congress","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Royal Military College of Canada","funders":"","keywords":"Global Positioning System; GPS/INS; Computer science; Inertial navigation system; GPS signals; Adaptive neuro fuzzy inference system; Genetic algorithm; Noise (video); Kalman filter; Real-time computing; Fuzzy logic; Assisted GPS; Fuzzy control system; Artificial intelligence; Mathematics; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008711554,0.0001295713,0.0001636239,0.0002789053,0.00006966171,0.00004453867,0.00008465817,0.00007247011,0.00003086348],"category_scores_gemma":[0.0000158253,0.0001408446,0.0000303317,0.0006316893,0.00002469175,0.0003799936,0.000008828841,0.00006637869,0.000007913126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002502057,"about_ca_system_score_gemma":0.00002701579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007714832,"about_ca_topic_score_gemma":0.00004308416,"domain_scores_codex":[0.9991032,0.00004431756,0.0004139619,0.0001294537,0.0001765101,0.0001325637],"domain_scores_gemma":[0.9994657,0.00001230181,0.0001271699,0.0001636347,0.000183263,0.00004793689],"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":[0.000006531173,0.00001491176,0.00005523623,0.0001081118,0.00001342552,0.000001866996,0.0001717135,0.984506,0.01282029,0.0006666809,0.000005664585,0.001629631],"study_design_scores_gemma":[0.0005339959,0.00001601782,0.001237282,0.0001865152,0.00002556191,0.000007774729,0.00004622567,0.9410865,0.05667344,0.00002542948,0.00002097237,0.0001402579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6377111,0.00004126552,0.3599859,0.000009546027,0.0006848099,0.0002506043,0.000008223071,0.0008091882,0.0004994099],"genre_scores_gemma":[0.9755107,0.000003224478,0.02422695,0.00000756591,0.00007774052,0.000008590279,0.0001138945,0.00003310671,0.00001819939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3377996,"threshold_uncertainty_score":0.5743477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008783709698204113,"score_gpt":0.2230111108597764,"score_spread":0.2142274011615723,"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."}}