{"id":"W2069119884","doi":"10.3390/s140917530","title":"Combined GPS/GLONASS Precise Point Positioning with Fixed GPS Ambiguities","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Key Research and Development Program of China; Hong Kong Polytechnic University; China Postdoctoral Science Foundation; Centre National d’Etudes Spatiales; National Natural Science Foundation of China","keywords":"Global Positioning System; Precise Point Positioning; GLONASS; Float (project management); Ambiguity resolution; Computer science; Ambiguity; Geodesy; Real-time computing; Algorithm; Remote sensing; Geography; Engineering; GNSS applications; 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":[0.00008110169,0.0001692004,0.0001726971,0.00007160212,0.0001067072,0.00008684762,0.0001061971,0.00006016793,0.00005631538],"category_scores_gemma":[0.00002745481,0.0001554235,0.00004239533,0.00009645177,0.00005821758,0.0001074166,0.00001609673,0.0001681884,0.0001331653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004566804,"about_ca_system_score_gemma":0.000006448693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004392132,"about_ca_topic_score_gemma":0.00001452666,"domain_scores_codex":[0.9992153,0.00004123612,0.0001689415,0.0001668749,0.0001457269,0.0002618885],"domain_scores_gemma":[0.9995365,0.00006688689,0.00002896479,0.0002263869,0.00006227938,0.00007903219],"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":[0.0005331439,0.000335024,0.007642674,0.0006940147,0.0007110818,0.00006488144,0.01211357,0.8257775,0.07301966,0.03450342,0.03360771,0.0109973],"study_design_scores_gemma":[0.004523718,0.002382753,0.05732857,0.00227045,0.0002054,0.0002469469,0.002271703,0.6221105,0.2959794,0.003929272,0.006115329,0.002635869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9361351,0.00002335921,0.003060667,0.0001183181,0.0002579883,0.0000896554,0.00000799353,0.0005438172,0.05976315],"genre_scores_gemma":[0.997175,0.000006310036,0.001379279,0.0000640406,0.00008450256,0.00001401709,0.00002478243,0.00003879706,0.001213269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2229598,"threshold_uncertainty_score":0.6337987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005873814666988294,"score_gpt":0.1813285750380435,"score_spread":0.1754547603710552,"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."}}