{"id":"W2750481671","doi":"10.1190/segam2017-17665308.1","title":"Multicomponent inverse scattering series internal multiple prediction in the τ-p domain","year":2017,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multiple; Scattering; Inverse scattering problem; Algorithm; Series (stratigraphy); Attenuation; Inverse; Amplitude; P wave; Computer science; Reflection (computer programming); Plane wave; Plane (geometry); Inverse problem; Total internal reflection; Geology; Optics; Mathematical analysis; Mathematics; Physics; Geometry; Arithmetic","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":[0.0003918471,0.0005937554,0.00025727,0.0002266011,0.0002097027,0.0006690361,0.0005912367,0.0005100814,0.002906511],"category_scores_gemma":[0.001089886,0.0002314493,0.0003319473,0.0003670226,0.0005014811,0.0008824394,0.0004708305,0.0008616089,0.0008837887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002158658,"about_ca_system_score_gemma":0.0004823805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001974838,"about_ca_topic_score_gemma":0.003036287,"domain_scores_codex":[0.9999076,0.00001848458,0.000004854381,0.00001614252,0.00004440355,0.000008372082],"domain_scores_gemma":[0.9997135,0.0001323473,0.0000307994,0.00004252912,0.00006923266,0.00001160402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000138961,0.00009309658,0.002047385,0.0001835823,0.00003246578,0.0004213706,0.0002444697,0.5884079,0.05277717,0.08252376,0.004374559,0.2687553],"study_design_scores_gemma":[0.00000285074,0.00001421869,0.0001081567,0.00000484986,0.000002196665,0.00004326576,0.00001518896,0.9895703,0.00439067,0.00452105,0.001323576,0.000003796403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01491276,0.00004954829,0.981822,0.00008803545,0.00003001478,0.0000170578,0.00003637032,0.0002867598,0.002757482],"genre_scores_gemma":[0.2668426,0.0003666082,0.7186612,0.00009169662,0.0000547201,0.00008243002,0.0002977772,0.0003122349,0.01329075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002906511,"threshold_uncertainty_score":0.009723186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02059022303512356,"score_gpt":0.2229446176930552,"score_spread":0.2023543946579316,"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."}}