{"id":"W2074376208","doi":"10.1002/2014gl061331","title":"Investigating high‐latitude ionospheric turbulence using global positioning system data","year":2014,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"GNSS positioning and interference","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Kurtosis; Skewness; Amplitude; Ionosphere; Turbulence; Wavelet; Probability density function; Physics; Global Positioning System; Geodesy; Phase (matter); Geophysics; Computational physics; Geology; Statistical physics; Meteorology; Statistics; Mathematics; Optics; Computer science; 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":[0.000437535,0.0002169363,0.0001539559,0.0009092076,0.0001988749,0.0003934002,0.0001311404,0.0002076767,0.0003180541],"category_scores_gemma":[0.0009569101,0.0001099936,0.0001491845,0.00129163,0.000142477,0.0003037433,0.0001616885,0.0001527009,0.00009685008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002837613,"about_ca_system_score_gemma":0.0002684002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01516116,"about_ca_topic_score_gemma":0.02195488,"domain_scores_codex":[0.9998423,0.00004055074,0.000009248928,0.00002744852,0.00004869654,0.00003165298],"domain_scores_gemma":[0.9996135,0.0001649996,0.00007715394,0.00003330419,0.0000806397,0.00003054428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003910594,0.000097179,0.8856617,0.00006007855,0.0001524108,0.0005389751,0.000362896,0.03356792,0.0452221,0.0007417413,0.0005764786,0.03262752],"study_design_scores_gemma":[0.00001224993,0.000092498,0.9632886,0.000006812083,0.00002483607,0.00006125213,0.0002010194,0.03321321,0.002579728,0.0001028249,0.0004038009,0.00001312645],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983714,0.00003256841,0.001030222,0.00002729124,0.000003351843,0.000003587518,0.0002152662,0.00001438562,0.0003019913],"genre_scores_gemma":[0.9989941,0.00003134349,0.0005293492,0.000004592309,0.000003796969,0.000001972671,0.0003687528,0.000002080425,0.00006402707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01516116,"threshold_uncertainty_score":0.03014582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0564223746257586,"score_gpt":0.3135717723800066,"score_spread":0.257149397754248,"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."}}