{"id":"W2808191774","doi":"10.3390/s18061910","title":"Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Defence Research and Development Canada","funders":"","keywords":"Sensor fusion; Kalman filter; Computer science; Residual; Context (archaeology); Acceleration; Monte Carlo method; Algorithm; Fault detection and isolation; Inertial frame of reference; Fusion; Fault (geology); Real-time computing; Artificial intelligence; Mathematics","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":[0.0008249379,0.0004490947,0.0005979514,0.0004527742,0.0004496081,0.0006877059,0.0009718266,0.000465298,0.00115176],"category_scores_gemma":[0.001391385,0.0002358051,0.000372887,0.0005153278,0.0003974775,0.001352203,0.0009245534,0.0006299519,0.0003884492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005702037,"about_ca_system_score_gemma":0.0005760468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001384448,"about_ca_topic_score_gemma":0.00141799,"domain_scores_codex":[0.9994466,0.0001196013,0.00004494987,0.0001244144,0.0001937452,0.00007081252],"domain_scores_gemma":[0.9993002,0.0001310948,0.0001038002,0.0001545789,0.0002806792,0.00002964879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004221413,0.0001421719,0.002559634,0.0002173825,0.0001173475,0.0001991899,0.0002792109,0.5097992,0.04049307,0.04166321,0.002939064,0.4011683],"study_design_scores_gemma":[0.00002751996,0.0002058625,0.0006358777,0.00001972293,0.00003237125,0.00007506915,0.0000455426,0.973788,0.01083982,0.01058601,0.003725125,0.00001921566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06606406,0.0009255109,0.9286812,0.0002682498,0.0001033569,0.00004532431,0.00005727629,0.0005650277,0.003290136],"genre_scores_gemma":[0.8527076,0.0003786129,0.1445418,0.00009647303,0.00008034054,0.00008775747,0.00009971461,0.00001801225,0.001989829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001384448,"threshold_uncertainty_score":0.004362762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07180134914243036,"score_gpt":0.2685737268192406,"score_spread":0.1967723776768102,"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."}}