{"id":"W2963183022","doi":"10.1007/s00211-019-01051-9","title":"Correcting for unknown errors in sparse high-dimensional function approximation","year":2019,"lang":"en","type":"article","venue":"Numerische Mathematik","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Pointwise; Lasso (programming language); Mathematics; A priori and a posteriori; Bounded function; Applied mathematics; Mathematical optimization; Focus (optics); Function approximation; Algorithm; Function (biology); Approximation algorithm; Computer science; Artificial intelligence; Mathematical analysis","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":[0.000181662,0.0001533793,0.0002191548,0.0001183404,0.00003262825,0.00002420535,0.00007884133,0.00009650573,0.00008850787],"category_scores_gemma":[0.00003135416,0.0001500348,0.00005588953,0.0001462532,0.000009474263,0.0001504947,0.00002057029,0.0001373341,0.0001024418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004458399,"about_ca_system_score_gemma":0.000008723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001496267,"about_ca_topic_score_gemma":0.000006812937,"domain_scores_codex":[0.9992242,0.00001647505,0.0002489523,0.0001799067,0.0001288146,0.0002016404],"domain_scores_gemma":[0.9995707,0.00007973791,0.00006223037,0.0002177294,0.00004217757,0.00002747179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003802155,0.0005094023,0.002622907,0.001344034,0.0003181088,0.00001184966,0.00351158,0.375364,0.4270121,0.0336707,0.03019843,0.1250567],"study_design_scores_gemma":[0.0007814583,0.0001025217,0.0008425858,0.0003524474,0.00002862527,0.00002731701,0.0002021446,0.8683017,0.1136824,0.01299898,0.002237292,0.0004424854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9005697,0.00007259782,0.09495255,0.00002440291,0.0006606484,0.0005237445,0.000002612511,0.0005803945,0.002613366],"genre_scores_gemma":[0.9746137,0.000001556243,0.02494839,0.00003180009,0.00005857724,0.00006809779,0.00002207991,0.00004531175,0.0002104968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4929377,"threshold_uncertainty_score":0.6118243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301465891576105,"score_gpt":0.2198687291655071,"score_spread":0.2068540702497461,"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."}}