{"id":"W1964159889","doi":"10.1002/j.2161-4296.2007.tb00406.x","title":"Ionosphere-Nullification Technique for Long-Baseline Real-Time Kinematic Applications","year":2007,"lang":"en","type":"article","venue":"NAVIGATION Journal of the Institute of Navigation","topic":"GNSS positioning and interference","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Office of Naval Research; National Oceanic and Atmospheric Administration; York University","keywords":"Baseline (sea); Ionosphere; Kinematics; Geodesy; Geology; Extrapolation; Computer science; Physics; Mathematics; Geophysics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001150255,0.0001262279,0.0001946519,0.00009594838,0.0001175514,0.00002554468,0.0002953886,0.0001110404,0.00000755591],"category_scores_gemma":[0.00009116702,0.0001044511,0.0001438095,0.0004023514,0.00008277463,0.0003951112,0.00001094133,0.0002015955,0.00001006738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001545713,"about_ca_system_score_gemma":0.0000629452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007426848,"about_ca_topic_score_gemma":0.000001797178,"domain_scores_codex":[0.9985875,0.00003027049,0.0008827831,0.00009229896,0.0002822175,0.0001248948],"domain_scores_gemma":[0.9982616,0.0001100646,0.0006090282,0.0002442453,0.0007158396,0.00005921797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009074657,0.0001756281,0.0003754609,0.0006668872,0.0001029588,0.000001339226,0.0003540918,0.2281419,0.7382085,0.006196922,0.0009348809,0.0247507],"study_design_scores_gemma":[0.0009331084,0.0001937581,0.003471909,0.002785079,0.0001918836,0.0002025412,0.00006907564,0.02805021,0.951194,0.01001677,0.002584467,0.0003071889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1580187,0.00008552509,0.8398721,0.0001412845,0.0004066282,0.0006774658,0.00001384491,0.00004784586,0.0007366142],"genre_scores_gemma":[0.9729543,0.00002137653,0.02653706,0.00001105137,0.000221453,0.00005469071,0.000098483,0.00001996233,0.00008163504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8149356,"threshold_uncertainty_score":0.4259392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01051642941803874,"score_gpt":0.2618742961081024,"score_spread":0.2513578666900636,"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."}}