{"id":"W1980442845","doi":"10.1017/s037346330500319x","title":"Navigation Kalman Filter Design for Pipeline Pigging","year":2005,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Chongqing University of Posts and Telecommunications","keywords":"Pigging; Odometer; Trajectory; Kalman filter; Pipeline (software); Smoothing; Computer science; Filter (signal processing); Extended Kalman filter; Computation; Control theory (sociology); Algorithm; Computer vision; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004990104,0.0001194313,0.0001661074,0.0001057698,0.00007032548,0.00003895471,0.00009064515,0.00008839557,0.0000178873],"category_scores_gemma":[0.00004335172,0.000112017,0.0001094204,0.0001491523,0.0000120587,0.0005871878,0.000004037681,0.0001808375,0.00001557162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001613967,"about_ca_system_score_gemma":0.00001627694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001676266,"about_ca_topic_score_gemma":5.932425e-7,"domain_scores_codex":[0.9989612,0.00003112659,0.0005429944,0.00007331472,0.0002256895,0.0001656997],"domain_scores_gemma":[0.9992774,0.00009482219,0.0001985711,0.00008029577,0.0002832733,0.0000656773],"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":[0.0001374817,0.00004620744,0.0000872237,0.00009948465,0.00004327256,0.000005861579,0.0008855995,0.6512712,0.2211446,0.0001617942,0.00886541,0.1172519],"study_design_scores_gemma":[0.001464374,0.0001936273,0.0004366669,0.0003521079,0.00008743429,0.0001349943,0.00005138006,0.6324238,0.3430651,0.001385232,0.02014932,0.0002559803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4511737,0.0002325287,0.5473301,0.0003223145,0.0005071253,0.0002098027,0.000005081928,0.00006349668,0.0001558427],"genre_scores_gemma":[0.9705272,0.00002403756,0.02759384,0.00005163463,0.001649893,0.000006145833,0.00004932823,0.0000304636,0.00006744635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5197362,"threshold_uncertainty_score":0.4567919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02077988042620239,"score_gpt":0.2581258271284425,"score_spread":0.2373459467022401,"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."}}