{"id":"W2112349215","doi":"10.1111/j.1365-2478.2011.01041.x","title":"Efficient least‐squares imaging with sparsity promotion and compressive sensing","year":2012,"lang":"en","type":"article","venue":"Geophysical Prospecting","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Curse of dimensionality; Compressed sensing; Dimensionality reduction; Computer science; Divide and conquer algorithms; Computational complexity theory; Mathematical optimization; Inverse problem; Algorithm; Inversion (geology); Mathematics; Artificial intelligence","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":[0.0001691757,0.000116935,0.000125731,0.00003984866,0.0003490055,0.00006461704,0.00004948387,0.00001889755,0.00005892916],"category_scores_gemma":[0.00002107562,0.00008851501,0.00002318173,0.0001194981,0.00012177,0.0001769272,0.00002129125,0.0001707378,0.00005808053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007051708,"about_ca_system_score_gemma":0.000007972455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001435602,"about_ca_topic_score_gemma":0.000003847319,"domain_scores_codex":[0.9991343,0.00004067437,0.00009322134,0.0002097261,0.0001807966,0.000341268],"domain_scores_gemma":[0.9996287,0.00005722746,0.00007294583,0.00009715751,0.00003723844,0.0001067694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003054768,0.00005565461,0.8613099,0.00003741014,0.00001043914,0.000008687627,0.001125362,0.0004008551,0.000750234,0.00009662255,0.0001288795,0.1360453],"study_design_scores_gemma":[0.0002662021,0.0001080749,0.5642707,0.0001142268,0.00002568823,0.0001282494,0.0005851845,0.4255673,0.007961798,0.0004604637,0.0002131866,0.0002989659],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935412,0.0001105243,0.002203339,0.0002768163,0.0001064282,0.0001516585,0.00000327781,0.0001520765,0.003454694],"genre_scores_gemma":[0.9961945,8.680199e-7,0.003388048,0.0001486197,0.000212525,2.888852e-7,0.000007070915,0.000004116506,0.00004397456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4251664,"threshold_uncertainty_score":0.3609537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110358065224517,"score_gpt":0.2055976890257472,"score_spread":0.194494108373502,"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."}}