{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004321789,0.0003878189,0.0003676277,0.0002368282,0.0002353164,0.0002711841,0.0002658556,0.0004513731,0.0007620608],"category_scores_gemma":[0.000992537,0.0001817308,0.0002598153,0.0002654598,0.000236742,0.0005311364,0.0003167418,0.0003278818,0.000178143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001870802,"about_ca_system_score_gemma":0.0006966381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004006448,"about_ca_topic_score_gemma":0.004488374,"domain_scores_codex":[0.9997914,0.00006132643,0.00001540574,0.00002861256,0.00008284846,0.00002041266],"domain_scores_gemma":[0.9997566,0.00008621028,0.00004216904,0.00002530626,0.00008143115,0.00000824593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003777065,0.0001005696,0.003280557,0.0002405525,0.00006859925,0.0001362326,0.0002744321,0.4208817,0.06975606,0.006168345,0.001293925,0.4974214],"study_design_scores_gemma":[0.0000135766,0.00009337269,0.0007873256,0.000007342447,0.00001371111,0.00004127302,0.00001902014,0.9864965,0.01035745,0.0005675211,0.001592099,0.00001082419],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04781294,0.0001579244,0.9508458,0.00005979091,0.00003618839,0.00001732912,0.000015758,0.0002897216,0.0007645398],"genre_scores_gemma":[0.6889575,0.0002536277,0.3082818,0.00003242245,0.00002472269,0.00005934976,0.00008458429,0.00003028928,0.002275749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004006448,"threshold_uncertainty_score":0.00796628,"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."}}