{"id":"W4379230580","doi":"10.3390/s23115292","title":"An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Ambiguity resolution; Ambiguity; Residual; Kinematics; GNSS applications; Computer science; Algorithm; Float (project management); Real Time Kinematic; Artificial intelligence; Engineering; Global Positioning System; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001212566,0.00009712015,0.00009853209,0.0001353894,0.000136545,0.00003286323,0.00008474787,0.0001157551,0.000008083841],"category_scores_gemma":[0.00003558845,0.0001049045,0.00004353731,0.0003357031,0.00002835937,0.000082659,0.00001275691,0.00007924892,0.00004787413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005420543,"about_ca_system_score_gemma":0.00000587661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002362484,"about_ca_topic_score_gemma":0.00001371954,"domain_scores_codex":[0.999383,0.00001102501,0.0001365289,0.0001395396,0.00007144279,0.0002584365],"domain_scores_gemma":[0.9996886,0.00002508217,0.00001668487,0.0001837695,0.00005100477,0.00003486332],"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":[0.00002902179,0.00004540693,0.0002069579,0.0001280154,0.00009880077,0.00001810583,0.001167464,0.3764985,0.0981968,0.001819744,0.01009923,0.511692],"study_design_scores_gemma":[0.0002472211,0.00004622931,0.001048137,0.000008134946,0.000007203923,0.000002065274,0.0003025576,0.9417762,0.05458716,0.0004978968,0.001340251,0.00013692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6482167,0.00005442262,0.3417304,0.0001428559,0.0009591439,0.0004210887,0.0001167187,0.007535183,0.0008234537],"genre_scores_gemma":[0.9843126,0.00004088179,0.01485829,0.00003217977,0.0001542139,0.00004683297,0.0002247937,0.00004743259,0.0002828003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5652778,"threshold_uncertainty_score":0.427788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047476850700949,"score_gpt":0.2400136708950384,"score_spread":0.229538902388029,"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."}}