{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00111874,0.0007204713,0.0008869489,0.0004194478,0.0004877255,0.0007829216,0.0008503056,0.0009623569,0.002809294],"category_scores_gemma":[0.002747478,0.0005454214,0.0005506142,0.0005543222,0.0003416602,0.0009011548,0.0006313695,0.001165617,0.00132097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007596764,"about_ca_system_score_gemma":0.001483342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01300943,"about_ca_topic_score_gemma":0.009899442,"domain_scores_codex":[0.9995727,0.0001008462,0.0000284612,0.0001247373,0.0001330879,0.0000401659],"domain_scores_gemma":[0.9993492,0.0002102231,0.00005688187,0.00004008438,0.0003285632,0.00001515231],"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":[0.0001381631,0.00002853019,0.0007931811,0.0002044238,0.00006041305,0.00006049585,0.0001480337,0.7460751,0.007868794,0.01147232,0.002623628,0.2305269],"study_design_scores_gemma":[0.00001445195,0.00003955642,0.0002518831,0.00001213923,0.000014521,0.00001393117,0.000008411108,0.9932703,0.001470837,0.002178704,0.002712059,0.00001321646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009394669,0.00007300123,0.9983971,0.00002166871,0.00001798293,0.00001146491,0.0000174191,0.000176787,0.0003452043],"genre_scores_gemma":[0.3876801,0.0008816167,0.6015236,0.0001509226,0.0001332073,0.0005711212,0.0005610356,0.0001984822,0.008299971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01300943,"threshold_uncertainty_score":0.0258674,"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."}}