{"id":"W2101343320","doi":"10.1190/geo2015-0043.1","title":"Waveform similarity for quality control of event locations, time picking, and moment tensor solutions","year":2015,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Schlumberger (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Microseismic Industry Consortium","keywords":"Microseism; Waveform; Geology; Similarity (geometry); Moment (physics); Multiplet; Seismology; Geodesy; Set (abstract data type); Algorithm; Computer science; Data mining; Physics; Spectral line; Artificial intelligence","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":[0.0004245461,0.00006523087,0.0001308794,0.00002590429,0.0001092571,0.00001224522,0.00007150875,0.00002931443,0.00002695139],"category_scores_gemma":[0.00006177203,0.00005494933,0.00003969821,0.00005353997,0.00009073005,0.0001035277,0.000008296071,0.00004512467,0.00002226549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007578385,"about_ca_system_score_gemma":0.00005726595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002543306,"about_ca_topic_score_gemma":0.00001437539,"domain_scores_codex":[0.9994129,0.0000317657,0.0001596184,0.0001145835,0.0001337677,0.0001473886],"domain_scores_gemma":[0.9994897,0.0001077303,0.00008304488,0.0001303511,0.0001170027,0.00007218592],"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":[0.0003566626,0.0005583873,0.2287768,0.0003514366,0.0002495465,0.000001685895,0.002983822,0.003361382,0.0006837628,0.01386027,0.1990287,0.5497876],"study_design_scores_gemma":[0.002949537,0.000948075,0.2051264,0.00007477401,0.0001457299,0.000006189732,0.0009574245,0.5238689,0.002920361,0.1135834,0.1487784,0.0006408541],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6726815,0.001441409,0.30326,0.0111806,0.0007263579,0.001859704,0.002076743,0.0003621225,0.006411506],"genre_scores_gemma":[0.99694,0.00001230076,0.001774294,0.0007950568,0.0000467526,0.000003230626,0.00009365196,0.000001760145,0.0003329764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5491467,"threshold_uncertainty_score":0.3844736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03860912765340138,"score_gpt":0.2606415514029227,"score_spread":0.2220324237495213,"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."}}