{"id":"W2128615445","doi":"10.3390/s121114344","title":"Inertial Aided Cycle Slip Detection and Identification for Integrated PPP GPS and INS","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Inertial navigation system; Precise Point Positioning; GPS/INS; Real-time computing; Computer science; Identification (biology); Navigation system; Mobile mapping; GPS signals; Automation; Simulation; Engineering; Inertial frame of reference; Assisted GPS; 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":[0.0003749882,0.0006788196,0.0005656409,0.00111378,0.0002241779,0.0003901688,0.000422723,0.0003262998,0.0008865386],"category_scores_gemma":[0.00103454,0.0002822891,0.0001731473,0.0009054871,0.000223056,0.00045337,0.0005634664,0.0003031055,0.0005474545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001818798,"about_ca_system_score_gemma":0.0004559905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00208875,"about_ca_topic_score_gemma":0.003293039,"domain_scores_codex":[0.9996498,0.00005559581,0.00002114381,0.00007517981,0.0001616582,0.00003661205],"domain_scores_gemma":[0.9996873,0.00005579411,0.00006570874,0.0000520027,0.0001235078,0.00001575214],"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":[0.0004208419,0.0001098851,0.01672917,0.0001247327,0.00006016086,0.0001390443,0.0001199213,0.07517347,0.07749058,0.001266651,0.001938089,0.8264275],"study_design_scores_gemma":[0.00004479902,0.0002365774,0.01771606,0.00001820879,0.00003925473,0.0002817591,0.00004988644,0.9472132,0.02991128,0.0008154977,0.00364538,0.00002803515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1163998,0.0002399348,0.8793353,0.00004271721,0.00006337564,0.00004936821,0.0001374533,0.001871341,0.001860749],"genre_scores_gemma":[0.6523418,0.0001507243,0.3454378,0.00003450441,0.00003943396,0.00005791416,0.0003792661,0.00005221945,0.001506293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00208875,"threshold_uncertainty_score":0.004153132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00939192957142769,"score_gpt":0.215725201596611,"score_spread":0.2063332720251834,"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."}}