{"id":"W1972416446","doi":"10.4236/pos.2015.61001","title":"PPP Accuracy Enhancement Using GPS/GLONASS Observations in Kinematic Mode","year":2015,"lang":"en","type":"article","venue":"Positioning","topic":"GNSS positioning and interference","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"GLONASS; Real Time Kinematic; Global Positioning System; GNSS applications; Geodesy; Satellite; Kinematics; Remote sensing; Computer science; Galileo (satellite navigation); Precise Point Positioning; Geography; Telecommunications; Engineering; Physics; Aerospace engineering","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":[0.0001155041,0.0001208851,0.0001317149,0.0001084198,0.00007451911,0.00008699748,0.0001048867,0.00004543041,0.00002487615],"category_scores_gemma":[0.00006414772,0.0001389692,0.00002856555,0.0002259318,0.00001485987,0.0003996157,0.00002455713,0.0001430149,0.00005300248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002629854,"about_ca_system_score_gemma":0.00002880234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001480699,"about_ca_topic_score_gemma":0.00002744033,"domain_scores_codex":[0.9991933,0.00002591992,0.0002751737,0.0001307844,0.0001476742,0.0002271415],"domain_scores_gemma":[0.9996195,0.00004743242,0.0000367529,0.0001499834,0.00007065773,0.00007567021],"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":[0.00000593191,0.00006485631,0.00154041,0.00007249616,0.00002597218,0.000006008825,0.002480121,0.9157035,0.07612137,0.003047804,0.0005275753,0.0004038963],"study_design_scores_gemma":[0.0003428211,0.00002977587,0.002688143,0.0005490894,0.00001583672,0.0000162172,0.0002176752,0.9787397,0.01544192,0.001672141,0.00006635839,0.0002203482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.868466,0.0001667748,0.1220299,0.00009923085,0.000277133,0.0001174525,0.000004890142,0.0001671645,0.008671433],"genre_scores_gemma":[0.9809133,0.000006676922,0.01880793,0.00006910992,0.00005901037,0.00003670283,0.00004053988,0.00002000291,0.00004675243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1124473,"threshold_uncertainty_score":0.5666999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08618091251015288,"score_gpt":0.3015251751388515,"score_spread":0.2153442626286986,"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."}}