{"id":"W2352320652","doi":"10.1007/s00024-016-1309-9","title":"GNSS Vertical Coordinate Time Series Analysis Using Single-Channel Independent Component Analysis Method","year":2016,"lang":"en","type":"article","venue":"Pure and Applied Geophysics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Jet Propulsion Laboratory; National Natural Science Foundation of China","keywords":"GNSS applications; Geodesy; Geology; Tectonics; Hilbert–Huang transform; SIGNAL (programming language); Global Positioning System; Series (stratigraphy); Seismology; Computer science; Telecommunications; White noise","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":[0.0001640989,0.0008024123,0.0004158447,0.0008254923,0.0003375106,0.0005447247,0.0003398628,0.0003591774,0.001387381],"category_scores_gemma":[0.000698064,0.0002309952,0.0007097844,0.001114407,0.0002197808,0.0006022201,0.0003621501,0.0006364769,0.0009066949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00017503,"about_ca_system_score_gemma":0.0007780975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003989602,"about_ca_topic_score_gemma":0.003350799,"domain_scores_codex":[0.9998165,0.00002702099,0.00001261465,0.00005369262,0.00007252525,0.00001770593],"domain_scores_gemma":[0.9998357,0.00002990381,0.00001570773,0.0000271194,0.00008566536,0.000005749043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002196961,0.00009452254,0.002963966,0.0002503073,0.0002050049,0.0001498849,0.0001085469,0.08001032,0.1111534,0.008841546,0.006269526,0.7897332],"study_design_scores_gemma":[0.0000376278,0.00007441982,0.01086878,0.00002774199,0.0001201654,0.000189722,0.0000561331,0.9323761,0.04230721,0.00469621,0.009181065,0.00006493433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01645786,0.0002266829,0.9805768,0.0000577402,0.0001282095,0.00003453801,0.0002689371,0.001051707,0.001197439],"genre_scores_gemma":[0.3096902,0.001059314,0.6802753,0.00006277935,0.0001758546,0.0002062819,0.001899944,0.0002741844,0.006356053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003989602,"threshold_uncertainty_score":0.007932782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01664554268775334,"score_gpt":0.2539588461433595,"score_spread":0.2373133034556062,"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."}}