{"id":"W2334986039","doi":"10.1190/segam2015-5923716.1","title":"High fidelity seismic trace interpolation","year":2015,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; TRACE (psycholinguistics); Regularization (linguistics); Permission; Interpolation (computer graphics); Algorithm; Seismic survey; Geology; Computer graphics (images); Seismology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006995471,0.0006699411,0.0005758129,0.001316185,0.0004097479,0.0009580937,0.001208787,0.0007361858,0.00832541],"category_scores_gemma":[0.003010747,0.0003054529,0.0006683863,0.001572212,0.0004167566,0.0008506622,0.001143762,0.001165411,0.002785207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005458122,"about_ca_system_score_gemma":0.001475654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02044696,"about_ca_topic_score_gemma":0.03228631,"domain_scores_codex":[0.9992544,0.00008111555,0.00003044169,0.0001065524,0.0004502916,0.00007716205],"domain_scores_gemma":[0.9992036,0.0001311454,0.00003918467,0.0002258432,0.0003668249,0.00003335187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002976049,0.00008752312,0.003690589,0.000242539,0.0000992911,0.0002339693,0.0001703212,0.3592674,0.0366338,0.01508152,0.01468572,0.5695098],"study_design_scores_gemma":[0.00001992484,0.00002235376,0.001036993,0.00002444161,0.000009539031,0.0001257318,0.00002864398,0.970594,0.01246081,0.003257267,0.01239035,0.00002998478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0197058,0.0001266286,0.9710128,0.0001091238,0.00007245757,0.00004162456,0.001327751,0.003482271,0.004121514],"genre_scores_gemma":[0.2108742,0.0001698422,0.7781723,0.00007596906,0.00004911541,0.00005703216,0.005181799,0.0007465805,0.004673227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02044696,"threshold_uncertainty_score":0.04065591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530322239226457,"score_gpt":0.2328230913092866,"score_spread":0.207519868917022,"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."}}