{"id":"W2766816162","doi":"10.1002/2017gl075342","title":"Seismic Interferometry Using Persistent Noise Sources for Temporal Subsurface Monitoring","year":2017,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Seismic interferometry; Microseism; Ambient noise level; Geology; Noise (video); Seismology; Seismic noise; Interferometry; Block (permutation group theory); Acoustics; Remote sensing; Computer science; Optics; Geomorphology; Sound (geography)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002280927,0.0002814574,0.0001727411,0.0009914794,0.0001325672,0.0003191546,0.0002441166,0.0002402344,0.0005398112],"category_scores_gemma":[0.0007090836,0.0001360376,0.0001253337,0.0009111202,0.0002090931,0.0004199611,0.0003834917,0.0002794256,0.0001076996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001919264,"about_ca_system_score_gemma":0.0002259601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001655302,"about_ca_topic_score_gemma":0.004357206,"domain_scores_codex":[0.9999187,0.00002006814,0.000003377281,0.00001794058,0.00003223904,0.000007753858],"domain_scores_gemma":[0.9997132,0.0001005699,0.00006552922,0.00004348056,0.00006092171,0.0000162396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006135132,0.0001986156,0.05941742,0.0001274945,0.0001292919,0.0003556062,0.0002119309,0.08125094,0.4452722,0.005260867,0.001529974,0.4056321],"study_design_scores_gemma":[0.00004926989,0.0001855416,0.06568331,0.00001688044,0.00006748424,0.000259607,0.00007760717,0.8851573,0.04310562,0.003405343,0.001952585,0.00003943475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6821882,0.0003420916,0.312517,0.0001799501,0.00003369175,0.00004253674,0.0004986739,0.0009264786,0.003271385],"genre_scores_gemma":[0.896157,0.0001123075,0.1031271,0.00001828028,0.00002327254,0.00002218097,0.0001819748,0.00003404449,0.0003238076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001655302,"threshold_uncertainty_score":0.003291309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010389580260802,"score_gpt":0.3398445795556639,"score_spread":0.2388056215295837,"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."}}