{"id":"W2771460011","doi":"10.1190/geo2017-0173.1","title":"Multidimensional inverse-scattering series internal multiple prediction in the coupled plane-wave domain","year":2017,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Penn West Exploration (Canada); University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Slowness; Benchmark (surveying); Waveform; Series (stratigraphy); Inverse; Plane (geometry); Time domain; Wavenumber; Amplitude; Reflection (computer programming); Plane wave; Domain (mathematical analysis); Algorithm; Multiple; Attenuation; Mathematical analysis; Frequency domain; Constant (computer programming); Mathematics; Computer science; Geometry; Geology; Physics; Optics; Seismology; Geodesy; Telecommunications","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":[0.0003670454,0.0005396473,0.0004580214,0.0002365965,0.0001824362,0.0006453324,0.000628666,0.0004664955,0.001964065],"category_scores_gemma":[0.001233813,0.0002098107,0.0004707127,0.0003831999,0.0003316215,0.0007502926,0.0004375344,0.000772015,0.0004338115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003935463,"about_ca_system_score_gemma":0.0006860093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004611117,"about_ca_topic_score_gemma":0.003899306,"domain_scores_codex":[0.999855,0.00002217635,0.000006198286,0.00002981474,0.00007160471,0.0000150558],"domain_scores_gemma":[0.9995949,0.0001799138,0.0000344582,0.00005903337,0.0001114587,0.00002038305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008944267,0.00004286328,0.001550491,0.00005379947,0.00001929643,0.0001082646,0.00005443323,0.9291162,0.007504239,0.01089143,0.001224638,0.04934491],"study_design_scores_gemma":[0.000002088556,0.000006586297,0.00005396395,0.000001530604,0.000001058798,0.000008244091,0.000004829965,0.9978384,0.001191768,0.0006639363,0.0002256113,0.000002074216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05258145,0.00006059128,0.9412987,0.0001229013,0.00004860264,0.0000244771,0.00009321455,0.0008481977,0.004921869],"genre_scores_gemma":[0.5992158,0.0001268659,0.3957662,0.00008034704,0.00002830732,0.00006947266,0.0004370337,0.0001782398,0.004097734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004611117,"threshold_uncertainty_score":0.009168506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0191929719399958,"score_gpt":0.2074242033605787,"score_spread":0.1882312314205828,"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."}}