{"id":"W4388807706","doi":"10.17794/rgn.2023.4.9","title":"THE PERFORMANCE ANALYSIS OF THE POST-MISSION WEB-BASED STATIC AND KINEMATIC PPP-AR SERVICE","year":2023,"lang":"en","type":"article","venue":"Rudarsko-geološko-naftni zbornik","topic":"GNSS positioning and interference","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada","keywords":"Precise Point Positioning; GNSS applications; Float (project management); Global Positioning System; GLONASS; Computer science; Real Time Kinematic; Geodesy; Kinematics; Real-time computing; Satellite; Service (business); Satellite system; Remote sensing; Geography; Telecommunications; Engineering; Systems engineering; Aerospace engineering; Business; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003262668,0.0008566469,0.0007437539,0.001165687,0.0005142422,0.001550693,0.001423641,0.0008897777,0.003506765],"category_scores_gemma":[0.01082188,0.0001669507,0.0004291772,0.001476476,0.0003686046,0.001830342,0.001026895,0.0006698211,0.002538569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278898,"about_ca_system_score_gemma":0.00122841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02548594,"about_ca_topic_score_gemma":0.006636362,"domain_scores_codex":[0.9962698,0.0005765656,0.0001740397,0.0004531888,0.001938226,0.0005881896],"domain_scores_gemma":[0.9928533,0.001840063,0.0005285618,0.0008220424,0.003657964,0.0002980451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.008001462,0.001610161,0.09094358,0.0008372865,0.0003707128,0.001139638,0.0004122806,0.3501,0.04085903,0.0043363,0.02154503,0.4798445],"study_design_scores_gemma":[0.00008421366,0.001264578,0.04171874,0.00002339834,0.00008298866,0.0003193841,0.0002548852,0.935616,0.01671903,0.0004945458,0.00336566,0.00005645362],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9055599,0.001617098,0.0610422,0.0004722583,0.0002452746,0.0003227283,0.002477389,0.008455231,0.01980796],"genre_scores_gemma":[0.9880515,0.0001985413,0.007252039,0.00007973512,0.00003061728,0.00005131519,0.002048351,0.0001413955,0.002146424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02548594,"threshold_uncertainty_score":0.05067515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007252785184800921,"score_gpt":0.2095761007659168,"score_spread":0.2023233155811159,"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."}}