{"id":"W2123460867","doi":"10.1190/tle31121496.1","title":"Noise suppression in surface microseismic data","year":2012,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Microseism; Noise (video); Waveform; Time domain; Offset (computer science); Acoustics; Noise suppression; Computer science; Seismology; Geology; Physics; Telecommunications; Artificial intelligence; Bandwidth (computing)","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":[0.0008321949,0.00007965694,0.0000904362,0.00003761009,0.0001091078,0.00003292364,0.0006085761,0.00003838301,0.0003041092],"category_scores_gemma":[0.00004128553,0.0000505713,0.000015782,0.0001384499,0.00007194969,0.0004163352,0.00005726971,0.0001827476,0.00116635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004897593,"about_ca_system_score_gemma":0.00001330093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002297838,"about_ca_topic_score_gemma":0.00002236731,"domain_scores_codex":[0.999243,0.00009154867,0.0001084639,0.0001447282,0.0001032364,0.0003090406],"domain_scores_gemma":[0.9992816,0.0001400278,0.00003460441,0.0004843573,0.000006067922,0.00005333453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001797154,0.00001975958,0.5043395,0.00001119755,0.00000377946,0.000002210367,0.001101451,0.0001342002,0.002397573,0.00001676651,0.4433907,0.04856493],"study_design_scores_gemma":[0.0002698748,0.00002505457,0.127079,0.00009822474,0.00001431738,0.00003547133,0.0003465953,0.06136069,0.02026667,0.0003731236,0.789828,0.0003030305],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826539,0.001810977,0.0001855727,0.002140108,0.0008423564,0.0001122344,0.00004604121,0.000121327,0.01208744],"genre_scores_gemma":[0.9957129,0.00007334362,0.0006871765,0.001679731,0.0001288495,1.527809e-7,0.00009687128,0.000003383402,0.001617567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3772604,"threshold_uncertainty_score":0.9996114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04935586330889279,"score_gpt":0.2739678756891524,"score_spread":0.2246120123802596,"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."}}