{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00118444,0.001214209,0.001301835,0.001645123,0.000393618,0.001140983,0.001168113,0.0008045058,0.0011975],"category_scores_gemma":[0.00244873,0.000388861,0.001272583,0.003015715,0.0003895856,0.001294848,0.001433996,0.0009368084,0.0009002918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003784488,"about_ca_system_score_gemma":0.0009144233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01212884,"about_ca_topic_score_gemma":0.01343631,"domain_scores_codex":[0.9983809,0.0003094189,0.00008091412,0.0004126864,0.0006658648,0.0001502002],"domain_scores_gemma":[0.9992613,0.0000956373,0.00007417215,0.0002744329,0.0002611141,0.00003329491],"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":[0.0007463711,0.0002742663,0.04561806,0.0007910546,0.001010255,0.0007575874,0.0003429705,0.3192539,0.03935747,0.003987808,0.01157051,0.5762897],"study_design_scores_gemma":[0.0003400848,0.0004935009,0.08150048,0.0001062474,0.0003872001,0.0007719646,0.0002931921,0.8606548,0.02466421,0.003574262,0.02703489,0.0001791198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4970332,0.002586369,0.4682223,0.0005121372,0.0005540058,0.0003911402,0.007332393,0.0072158,0.01615258],"genre_scores_gemma":[0.7224326,0.0005421975,0.2661192,0.0001555894,0.000129974,0.0001556061,0.008051496,0.0002048779,0.002208554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01212884,"threshold_uncertainty_score":0.02411646,"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."}}