{"id":"W2085042581","doi":"10.1007/s00024-011-0404-1","title":"Identification of Glacial Isostatic Adjustment in Eastern Canada Using S Transform Filtering of GPS Observations","year":2011,"lang":"en","type":"article","venue":"Pure and Applied Geophysics","topic":"GNSS positioning and interference","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada; Western University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Post-glacial rebound; Global Positioning System; Noise (video); Geology; Computer science; Glacial period; Identification (biology); Random noise; Data processing; Process (computing); Geodesy; Algorithm; SIGNAL (programming language); Data mining; Remote sensing; Artificial intelligence; Telecommunications; Geomorphology; Image (mathematics)","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":[0.00001934131,0.0000569041,0.00009365482,0.00002138392,0.00001353518,0.000003261518,0.00003993575,0.00001740655,0.000002657495],"category_scores_gemma":[7.121257e-7,0.00006230576,0.0000103902,0.00007677943,0.00001328262,0.00004867357,0.000005472007,0.00004125017,2.854318e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002586302,"about_ca_system_score_gemma":0.00002462629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01247029,"about_ca_topic_score_gemma":0.009409379,"domain_scores_codex":[0.9996229,0.000002227842,0.0001860549,0.00005770112,0.00005877671,0.00007236275],"domain_scores_gemma":[0.9998671,0.00000639413,0.00003385612,0.00006182295,0.00001590794,0.00001496601],"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":[0.00004843367,0.0001439571,0.003399112,0.001288738,0.0001019662,0.000001030551,0.01988375,0.03326914,0.8938115,0.01980989,0.0000424527,0.02820001],"study_design_scores_gemma":[0.0006522875,0.0000565409,0.2482991,0.0003181425,0.00008572631,0.000001637586,0.001418887,0.1506458,0.5904374,0.007677029,0.0000270779,0.0003804624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931453,0.00002791791,0.005625291,0.000002456764,0.00005681912,0.00007812449,0.00001787249,0.000007995244,0.001038225],"genre_scores_gemma":[0.9996584,0.000007537205,0.0002880912,0.000004406428,0.00001204816,0.000009136805,0.000007652357,0.000006092523,0.000006643776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3033742,"threshold_uncertainty_score":0.9941058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02564457434772857,"score_gpt":0.1990917307729352,"score_spread":0.1734471564252066,"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."}}