{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007107801,0.0008305304,0.0004692459,0.0006831168,0.0002299222,0.0006489562,0.0005380615,0.000405818,0.0007171555],"category_scores_gemma":[0.001709378,0.0003452708,0.000649363,0.001016313,0.0002232884,0.001044133,0.001113687,0.0004697149,0.0005970275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002595895,"about_ca_system_score_gemma":0.0007071627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00472361,"about_ca_topic_score_gemma":0.004572217,"domain_scores_codex":[0.9993729,0.0001203234,0.00004476399,0.0001196241,0.0002847143,0.00005766202],"domain_scores_gemma":[0.9996087,0.00005802706,0.00005959129,0.00009161165,0.0001701752,0.0000119821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003915332,0.00007537306,0.02906021,0.00032007,0.0002105898,0.0004593286,0.0002776972,0.2505694,0.06755226,0.005982981,0.003176987,0.6419234],"study_design_scores_gemma":[0.00008497904,0.0002279646,0.02382104,0.00005451323,0.0002463381,0.0004999852,0.0001180675,0.9145345,0.04300069,0.002577294,0.01475516,0.00007951555],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08815593,0.0005145206,0.9049171,0.0001236247,0.0001580123,0.00004092066,0.0002436186,0.001710915,0.004135405],"genre_scores_gemma":[0.7506526,0.0007201495,0.2451217,0.00008197218,0.000080643,0.00003905529,0.0009905583,0.0001342376,0.002179267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00472361,"threshold_uncertainty_score":0.009392202,"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."}}