{"id":"W3038608504","doi":"10.1186/s43020-020-00022-y","title":"A method of improving ambiguity fixing rate for post-processing kinematic GNSS data","year":2020,"lang":"en","type":"article","venue":"Satellite Navigation","topic":"GNSS positioning and interference","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Fund for Distinguished Young Scholars; Foundation for Distinguished Young Talents in Higher Education of Guangdong; York University","keywords":"Ambiguity resolution; Ambiguity; Computer science; GNSS applications; Integer (computer science); Algorithm; Position (finance); Smoothing; Real Time Kinematic; Global Positioning System; Artificial intelligence; Computer vision; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001234317,0.001267186,0.0006389186,0.001530417,0.0005378225,0.0007752857,0.001104925,0.0005703581,0.001225599],"category_scores_gemma":[0.002714203,0.0005016613,0.0006437617,0.001179773,0.0005197852,0.000997092,0.0008557543,0.0009926684,0.0008378956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003521382,"about_ca_system_score_gemma":0.0009100201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002035948,"about_ca_topic_score_gemma":0.001770199,"domain_scores_codex":[0.998646,0.0001956124,0.0000909646,0.0002817128,0.0007053955,0.00008044979],"domain_scores_gemma":[0.9989319,0.000233529,0.0001180152,0.0001892143,0.0004975485,0.00002976751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003343462,0.0001110409,0.002520734,0.000202361,0.0001044547,0.0001135468,0.0002113144,0.03776593,0.1791356,0.008303137,0.001537009,0.7696604],"study_design_scores_gemma":[0.00008342502,0.000285262,0.004791978,0.00004182483,0.0001795892,0.0004545742,0.00006559656,0.7859962,0.1929441,0.002731021,0.01231133,0.0001151198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01035914,0.0002009816,0.9881755,0.0000289208,0.00005691235,0.00003019708,0.00001493006,0.0004612894,0.0006721123],"genre_scores_gemma":[0.1915603,0.0003525023,0.8055708,0.0000557662,0.00009142652,0.00007778182,0.0001303723,0.0001554968,0.002005429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002035948,"threshold_uncertainty_score":0.006527781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05457907819544223,"score_gpt":0.302290356500487,"score_spread":0.2477112783050447,"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."}}