{"id":"W2002823602","doi":"10.1080/10739149.2012.673192","title":"AUGMENTED FAST ORTHOGONAL SEARCH/KALMAN FILTERING (FOS/KF) POSITIONING AND ORIENTATION SOLUTION USING MEMS-BASED INERTIAL NAVIGATION SYSTEM (INS) IN DRILLING APPLICATIONS","year":2012,"lang":"en","type":"article","venue":"Instrumentation Science & Technology","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Kalman filter; Microelectromechanical systems; Inertial navigation system; Orientation (vector space); Computer science; Process (computing); Inertial frame of reference; Inertial measurement unit; Control theory (sociology); Simulation; Artificial intelligence; Materials science; Mathematics; Physics","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":[0.0005479459,0.0001611899,0.0001440303,0.001157127,0.0004801185,0.00008861336,0.000139592,0.0001341998,0.000006985271],"category_scores_gemma":[0.00001395148,0.0001879878,0.00002236975,0.002257516,0.0002801438,0.001247831,0.00004448709,0.0002048971,0.000008118795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008502741,"about_ca_system_score_gemma":0.00004938723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007688184,"about_ca_topic_score_gemma":0.000009723298,"domain_scores_codex":[0.9984274,0.00003172443,0.000434708,0.0002881755,0.0003606071,0.0004574111],"domain_scores_gemma":[0.9995058,0.00001885778,0.0001015335,0.0001597432,0.0001277665,0.00008625365],"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":[0.00001021524,0.00002271859,0.0160274,0.0000598279,0.000004807704,9.929842e-7,0.0005156978,0.06150825,0.8986477,0.002552327,2.968027e-7,0.02064974],"study_design_scores_gemma":[0.0005450332,0.000032742,0.00702508,0.0001503786,0.0000151084,0.00004790821,0.001454372,0.5241086,0.4663558,0.00004928713,0.00001612659,0.0001995648],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7697855,0.00004796993,0.2290967,0.00004019449,0.0002459502,0.0003817211,0.000006662343,0.000307116,0.00008825657],"genre_scores_gemma":[0.9923568,0.000004270433,0.007323975,0.00001470843,0.00008142296,0.0001006642,0.0000975181,0.00001941477,0.000001207738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4626004,"threshold_uncertainty_score":0.7665918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491110020855752,"score_gpt":0.2750611911034114,"score_spread":0.2601500908948539,"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."}}