{"id":"W4400810668","doi":"10.1109/tgrs.2024.3431439","title":"5-D Seismic Data Interpolation by Continuous Representation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Interpolation (computer graphics); Computer science; Representation (politics); Geology; Nearest-neighbor interpolation; Multivariate interpolation; Bilinear interpolation; Remote sensing; Artificial intelligence; Computer vision","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":[0.0002682329,0.00009521753,0.00008596074,0.0001520157,0.0002758607,0.0002195858,0.0001263536,0.00004651439,0.00005378909],"category_scores_gemma":[0.000007284865,0.00007920064,0.00002463419,0.0003128854,0.0001538941,0.0005861508,0.000001261707,0.0001733848,0.00005120345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005134056,"about_ca_system_score_gemma":0.00002935233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008589619,"about_ca_topic_score_gemma":0.0000658377,"domain_scores_codex":[0.9990456,0.00004529656,0.0001393097,0.0004193288,0.0001810545,0.0001694813],"domain_scores_gemma":[0.9995478,0.0001026971,0.00002502719,0.0002432481,0.00001845065,0.00006275711],"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":[0.000006087741,0.00000222115,0.000006924245,0.000006810505,0.000003958975,0.000005102621,0.0001466817,0.00009582099,0.0006512855,3.173364e-7,0.002897386,0.9961774],"study_design_scores_gemma":[0.00004539186,0.00004738802,0.00007507342,0.00009906725,0.00001458508,0.00008707921,0.0002531846,0.9855614,0.004899754,0.0002366647,0.008576238,0.0001041685],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0465765,0.0002528072,0.9498437,0.00118634,0.0009548011,0.00008740648,0.00007364047,0.0002305375,0.0007942552],"genre_scores_gemma":[0.9827693,0.0003148718,0.01510261,0.000730866,0.00003575046,1.215251e-8,0.00004121859,0.000003791996,0.001001623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9960732,"threshold_uncertainty_score":0.9980122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0243971494365471,"score_gpt":0.2627799499593745,"score_spread":0.2383828005228274,"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."}}