{"id":"W2403662403","doi":"10.3390/s16060779","title":"Precise Point Positioning Using Triple GNSS Constellations in Various Modes","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Constellation; Precise Point Positioning; Computer science; Global Positioning System; Point (geometry); Hybrid positioning system; Remote sensing; Real-time computing; Telecommunications; Geodesy; Positioning system; Geography; Physics; Astronomy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005925972,0.0006797101,0.0005141802,0.0007336829,0.0002602341,0.0009447162,0.0009208017,0.0006209775,0.001016203],"category_scores_gemma":[0.00113912,0.0003349945,0.000590252,0.001528324,0.0005476051,0.001392674,0.001182886,0.0006191839,0.0007113995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006783918,"about_ca_system_score_gemma":0.0006669089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007364884,"about_ca_topic_score_gemma":0.006621039,"domain_scores_codex":[0.9992342,0.0001212142,0.00002729556,0.0001946167,0.0003580443,0.00006466354],"domain_scores_gemma":[0.9997419,0.00004349956,0.0000391401,0.00008972615,0.00006969145,0.000016061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002028287,0.00003672563,0.01295734,0.0001011216,0.00008859157,0.0002165842,0.0002310733,0.8452839,0.01433129,0.0140624,0.001841735,0.1106464],"study_design_scores_gemma":[0.00002392831,0.0001320038,0.005878826,0.0000281007,0.00003338421,0.0002065831,0.00009167777,0.9728032,0.006491075,0.006975172,0.007286785,0.00004924663],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1103932,0.0003350034,0.87863,0.0002009325,0.0001070053,0.00006062155,0.0007650927,0.001156481,0.008351593],"genre_scores_gemma":[0.8406687,0.000551707,0.1519371,0.0000973054,0.00004425848,0.00009900433,0.001591076,0.0001382385,0.004872644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007364884,"threshold_uncertainty_score":0.01464403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159708991584326,"score_gpt":0.2247071141015682,"score_spread":0.2087362149431356,"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."}}