{"id":"W3091203820","doi":"10.1190/segam2020-3420645.1","title":"Event detection using a fast matched filter algorithm – An efficient way to deal with big microseismic data sets","year":2020,"lang":"en","type":"article","venue":"","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Microseism; Computer science; Big data; Filter (signal processing); Event (particle physics); Algorithm; Data mining; Geology; Seismology; Computer vision","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":[0.001522339,0.001056968,0.0008014762,0.002861324,0.0007234497,0.001320685,0.0009883002,0.001038023,0.00305666],"category_scores_gemma":[0.004455692,0.0004587611,0.0008769149,0.002126754,0.0003714694,0.001987131,0.001185402,0.0008844938,0.00150014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004597406,"about_ca_system_score_gemma":0.001179097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00263071,"about_ca_topic_score_gemma":0.003236532,"domain_scores_codex":[0.9989156,0.0001225368,0.00008250633,0.0002312229,0.0005622546,0.00008587725],"domain_scores_gemma":[0.9985819,0.0005146667,0.0001314407,0.0002283417,0.0004878746,0.00005587652],"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":[0.0003251884,0.0001749026,0.005584777,0.0002177832,0.0001633206,0.0003926083,0.000222928,0.02981769,0.08851621,0.005444613,0.007672763,0.8614672],"study_design_scores_gemma":[0.00006675719,0.000220719,0.01023885,0.00004674272,0.00007652151,0.000797588,0.000193142,0.862072,0.09195106,0.009808587,0.02443285,0.00009520521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01167549,0.0001706245,0.9845426,0.0001089478,0.000081163,0.00006834225,0.0001449677,0.002448043,0.0007598854],"genre_scores_gemma":[0.08725075,0.0002116956,0.9096818,0.0001113573,0.00007660871,0.0001281216,0.0005633709,0.0002226482,0.00175364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00305666,"threshold_uncertainty_score":0.01022547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05365424752482961,"score_gpt":0.2740098916415461,"score_spread":0.2203556441167165,"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."}}