{"id":"W2904372252","doi":"10.1190/igc2018-311","title":"Case study on coda duration magnitudes calibration with the use of local seismological networks","year":2018,"lang":"en","type":"article","venue":"International Geophysical Conference, Beijing, China, 24-27 April 2018","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Coda; Duration (music); Calibration; Seismology; Geology; Statistics; Mathematics; Physics; Acoustics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002566196,0.0002996651,0.0003148226,0.0001070436,0.0004007601,0.0002613604,0.0007601455,0.0001203734,0.00006459242],"category_scores_gemma":[0.00006823196,0.000187687,0.00008891703,0.0003244156,0.0009130419,0.0006433653,0.0003660636,0.0003901071,0.00006020492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003753765,"about_ca_system_score_gemma":0.00008882303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007694689,"about_ca_topic_score_gemma":0.0003350205,"domain_scores_codex":[0.9978111,0.0002844961,0.0004039183,0.0005964685,0.0005862554,0.0003177738],"domain_scores_gemma":[0.9982032,0.000410315,0.0002912761,0.0005175243,0.0004960472,0.00008161397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003011551,0.009103819,0.08025331,0.00004659205,0.002969566,0.004780906,0.02357853,0.02543832,0.0002976003,0.697335,0.04648424,0.1067005],"study_design_scores_gemma":[0.002057993,0.007545425,0.2079934,0.0000857294,0.000115357,0.0009010253,0.001394023,0.7658659,0.0003578481,0.00367717,0.009198569,0.0008075388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5535019,0.000004536147,0.4431533,0.001698164,0.0008347951,0.0002505531,0.000006060516,0.00006643454,0.000484196],"genre_scores_gemma":[0.9963245,0.000003718946,0.001102719,0.001145813,0.0004991636,0.0000343687,0.00001379217,0.00001162223,0.0008643487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7404276,"threshold_uncertainty_score":0.7653651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05339331417883033,"score_gpt":0.2640649520147438,"score_spread":0.2106716378359135,"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."}}