{"id":"W2616943786","doi":"10.3997/2214-4609.201701226","title":"Transmission Multicomponent Interferometric Extended Source Velocity Analysis","year":2017,"lang":"en","type":"article","venue":"Proceedings","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"Measure (data warehouse); Seismic interferometry; Interferometry; Reflection (computer programming); Inverse problem; Wavelet; Inverse; Transmission (telecommunications); Mathematical analysis; Field (mathematics); Physics; Optics; Mathematics; Computer science; Geometry","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":[0.0003188334,0.0001188432,0.0001903999,0.0003903917,0.0006429055,0.0002696746,0.000489021,0.00005602074,0.0007548113],"category_scores_gemma":[0.00008831263,0.00009410637,0.0001236147,0.0003242129,0.00009708695,0.0003894634,0.00002330229,0.000146028,0.00008446314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006918591,"about_ca_system_score_gemma":0.000008516216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003199704,"about_ca_topic_score_gemma":0.000004454826,"domain_scores_codex":[0.9990913,0.000006065929,0.0001566862,0.0002803477,0.0002368939,0.0002287022],"domain_scores_gemma":[0.9994887,0.00002192872,0.0001395781,0.0001518993,0.00006826853,0.0001296968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003385808,0.00002336842,0.254919,0.0000212318,0.00007630153,0.000001595324,0.0004491215,0.000009722894,0.0003463544,0.000010485,0.002098295,0.7420107],"study_design_scores_gemma":[0.0002307118,0.00007526949,0.6494995,0.00002668981,0.0001550953,0.000005278195,0.0001564608,0.3006988,0.009381436,0.0003435707,0.03920775,0.0002194173],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677663,0.0001731536,0.009435334,0.001271777,0.0001132261,0.0001187291,0.000006224636,0.0002674492,0.02084778],"genre_scores_gemma":[0.9939219,0.00006326918,0.004933936,0.0003090003,0.00004108949,8.724041e-7,0.00001100912,0.000003066137,0.0007158205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7417913,"threshold_uncertainty_score":0.8264654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028462211409681,"score_gpt":0.2437774763260873,"score_spread":0.2234928542119905,"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."}}