{"id":"W2108849537","doi":"10.1007/s10291-004-0105-7","title":"An analysis of carrier phase differential kinematic GPS positioning using DynaPos","year":2004,"lang":"en","type":"article","venue":"GPS Solutions","topic":"GNSS positioning and interference","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Global Positioning System; Kinematics; Geodesy; Differential GPS; Remote sensing; Precise Point Positioning; Range (aeronautics); Differential (mechanical device); Phase (matter); Real Time Kinematic; Computer science; Geology; Physics; Aerospace engineering; Telecommunications; Engineering; GNSS applications","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":[0.0002778707,0.0003210678,0.0002342066,0.00071742,0.0003938351,0.0007578499,0.0002672626,0.0002104594,0.001980105],"category_scores_gemma":[0.001114927,0.0002231481,0.0002348508,0.0009507707,0.0002102552,0.0004980248,0.0003249271,0.0002248876,0.0005088176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008600774,"about_ca_system_score_gemma":0.0005473243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01307185,"about_ca_topic_score_gemma":0.01442829,"domain_scores_codex":[0.9997036,0.00003764817,0.000007761763,0.00003768521,0.0001736713,0.0000396405],"domain_scores_gemma":[0.9996343,0.0001751849,0.00002837146,0.00002219134,0.0001319635,0.000008077419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006033826,0.00005238013,0.04020403,0.0002245509,0.0001101703,0.0006636196,0.000251963,0.6925933,0.03174669,0.02788491,0.001920094,0.2037449],"study_design_scores_gemma":[0.00001815098,0.0001505433,0.03490384,0.00002146745,0.00008625094,0.0003288268,0.0001664771,0.9480388,0.008428508,0.002230443,0.005603869,0.0000229639],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.639808,0.0008207978,0.3244829,0.0002246042,0.0000818913,0.00005215688,0.0005499967,0.0005897214,0.03338988],"genre_scores_gemma":[0.9765819,0.0003357923,0.01612505,0.00002242428,0.00002000244,0.00001303763,0.0003336468,0.00006272084,0.006505461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01307185,"threshold_uncertainty_score":0.0259915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364892397915893,"score_gpt":0.2860257050153718,"score_spread":0.2623767810362129,"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."}}