{"id":"W2137580029","doi":"10.1190/1.3627492","title":"Uncertainty in surface microseismic monitoring","year":2011,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"Microseism; Event (particle physics); Computer science; Noise (video); Position (finance); Measurement uncertainty; Data mining; Artificial intelligence; Geology; Seismology; Statistics; Mathematics; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001684511,0.00006480758,0.00007066916,0.00004882758,0.00004080974,0.00001257511,0.0001379745,0.00003234061,0.002580531],"category_scores_gemma":[0.00001029854,0.00005237118,0.00002081916,0.0001233557,0.00003675438,0.0001299025,0.000005991197,0.00008715577,0.0002718762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003923486,"about_ca_system_score_gemma":0.00001859352,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06651682,"about_ca_topic_score_gemma":0.0001047631,"domain_scores_codex":[0.9994853,0.00002373909,0.0001034524,0.000134377,0.00007033152,0.000182783],"domain_scores_gemma":[0.9997863,0.0000226785,0.00001842155,0.0001043204,0.00001072458,0.00005750934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000993481,0.000007113033,0.9690972,0.000002491945,0.000001472813,0.000007911699,0.000375455,0.0002811112,0.0001290855,0.00001323564,0.001925376,0.0281496],"study_design_scores_gemma":[0.0003741036,0.0001066644,0.8540142,0.00005324137,0.000004462294,0.00001694731,0.001846159,0.0471754,0.07272507,0.003370661,0.01990268,0.0004103713],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9260424,0.0001328119,0.00008231668,0.00008346957,0.0002122943,0.00004642279,0.000002709077,0.0001229819,0.07327459],"genre_scores_gemma":[0.9932838,0.00004554825,0.005345342,0.0005250084,0.00001712641,1.305417e-7,0.000003821454,0.000001475582,0.0007777231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.115083,"threshold_uncertainty_score":0.9983312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331938367944225,"score_gpt":0.2241255956490172,"score_spread":0.1909317588545947,"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."}}