{"id":"W2573739355","doi":"10.1016/j.asr.2017.01.011","title":"The Multi-GNSS Experiment (MGEX) of the International GNSS Service (IGS) – Achievements, prospects and challenges","year":2017,"lang":"en","type":"article","venue":"Advances in Space Research","topic":"GNSS positioning and interference","field":"Engineering","cited_by":851,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"GNSS applications; GLONASS; Quasi-Zenith Satellite System; Galileo (satellite navigation); Global Positioning System; Computer science; Satellite system; Constellation; Service (business); Precise Point Positioning; Satellite; Remote sensing; Systems engineering; Telecommunications; Geography; Engineering; Aerospace engineering; Business","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":[0.005937401,0.0008495245,0.0008303472,0.0005330564,0.0006727948,0.001639726,0.0007537926,0.001361298,0.001770558],"category_scores_gemma":[0.001665106,0.000196408,0.0003859744,0.0005446847,0.001914035,0.002432347,0.004108417,0.002042551,0.0005469394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009726148,"about_ca_system_score_gemma":0.002104876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001763626,"about_ca_topic_score_gemma":0.001944315,"domain_scores_codex":[0.9984503,0.0005217802,0.00003093523,0.0002485857,0.0004745782,0.0002738652],"domain_scores_gemma":[0.9986676,0.0002559875,0.0001240153,0.0003331546,0.0002343452,0.0003848952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.009038685,0.001602442,0.07011293,0.001593506,0.0006005498,0.001348387,0.002022967,0.02145182,0.1607317,0.2207256,0.04352153,0.46725],"study_design_scores_gemma":[0.001037145,0.00722855,0.1573886,0.0008764339,0.0002782586,0.001838151,0.00192752,0.02974723,0.1372277,0.09340392,0.5687038,0.0003427494],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5932615,0.04752908,0.1134239,0.03638925,0.007306115,0.0009405598,0.005434541,0.002651694,0.1930634],"genre_scores_gemma":[0.9113278,0.005831306,0.0610508,0.0035884,0.001221731,0.000258897,0.002464186,0.0003134853,0.01394347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005937401,"threshold_uncertainty_score":0.03140032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09295504454857166,"score_gpt":0.3861753154010906,"score_spread":0.2932202708525189,"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."}}