{"id":"W2282264169","doi":"10.1093/gji/ggv419","title":"Comparison of the STA/LTA and power spectral density methods for microseismic event detection","year":2015,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Microseismic Industry Consortium","keywords":"Geophone; Microseism; Event (particle physics); Computer science; Spectral density; Term (time); Filter (signal processing); Data mining; Pattern recognition (psychology); Algorithm; Geology; Artificial intelligence; Seismology; Computer vision; Physics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003912915,0.00007002323,0.0001374716,0.00003632784,0.0001342875,0.00004908804,0.0003508299,0.00002703313,0.000002402267],"category_scores_gemma":[0.0001402219,0.00004816367,0.00008876382,0.00006518195,0.00009266564,0.0001562137,0.0001782299,0.0001865907,0.000002587981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003619174,"about_ca_system_score_gemma":0.00003972092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001806087,"about_ca_topic_score_gemma":0.000005239975,"domain_scores_codex":[0.999289,0.0001079605,0.0001935578,0.0001240232,0.0001649438,0.0001204723],"domain_scores_gemma":[0.9992974,0.0001491796,0.0001522241,0.00009873319,0.0002465095,0.00005592402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001309769,0.001998711,0.07273682,0.00002941525,0.001592434,0.0000164323,0.0139748,0.002307327,0.125497,0.126254,0.01181013,0.6424731],"study_design_scores_gemma":[0.00175151,0.001118954,0.587672,0.00003156728,0.00004269119,0.0004233812,0.0003852217,0.09038351,0.09593471,0.2114512,0.01055477,0.0002504631],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4727203,0.0000667206,0.5241452,0.001678255,0.001280918,0.00004258756,0.00000120979,0.000005622866,0.00005917199],"genre_scores_gemma":[0.9749334,0.000002980818,0.02460037,0.0002109954,0.0001654103,0.00000201628,2.735422e-7,0.000002434814,0.00008211236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6422226,"threshold_uncertainty_score":0.1964057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03839265672517489,"score_gpt":0.3796254407431983,"score_spread":0.3412327840180234,"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."}}