{"id":"W3000848722","doi":"10.1109/icsidp47821.2019.9173062","title":"GNSS Precise Point Positioning with Android Smartphones and Comparison with High Performance Receivers","year":2019,"lang":"en","type":"article","venue":"2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)","topic":"GNSS positioning and interference","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; GLONASS; Precise Point Positioning; Computer science; Global Positioning System; Pseudorange; Real-time computing; Multipath propagation; Remote sensing; Geodesy; Telecommunications; Geography","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":[0.00037965,0.0004666078,0.0002879004,0.001119385,0.0001814639,0.0004665387,0.0003131668,0.0004441724,0.002892506],"category_scores_gemma":[0.001652369,0.0001029155,0.0001784822,0.001092981,0.0001646131,0.0004389466,0.000358159,0.0001815157,0.001543921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002155797,"about_ca_system_score_gemma":0.0001893829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007899605,"about_ca_topic_score_gemma":0.00877625,"domain_scores_codex":[0.9991896,0.0001241774,0.00005473409,0.000120431,0.0004224659,0.00008856397],"domain_scores_gemma":[0.9992035,0.0001654493,0.00007195564,0.0001215749,0.0004079835,0.00002963176],"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":[0.004792076,0.0004799886,0.127308,0.001534664,0.0002402967,0.001601038,0.001040431,0.05088995,0.2125479,0.002490904,0.01231279,0.584762],"study_design_scores_gemma":[0.0002298817,0.005227247,0.7208421,0.0001776147,0.0003003293,0.002885025,0.001786632,0.1427478,0.09723853,0.0006600959,0.02766587,0.0002389931],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9632501,0.000635397,0.01788512,0.0001225231,0.0001177085,0.0001329606,0.002361583,0.001315381,0.01417925],"genre_scores_gemma":[0.9802839,0.0003848852,0.01228376,0.00004158279,0.00003265514,0.00006138204,0.002600946,0.00008032801,0.004230595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007899605,"threshold_uncertainty_score":0.01570725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746479226105102,"score_gpt":0.2551391250640951,"score_spread":0.2276743328030441,"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."}}