{"id":"W4319943411","doi":"10.1007/s43670-023-00047-9","title":"Publisher Correction: NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems","year":2023,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Inverse; Artificial neural network; Computer science; Inverse problem; Artificial intelligence; Algorithm; Mathematics; Mathematical analysis; Geometry","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":[0.0008555925,0.0002156402,0.0003673892,0.000483573,0.0004757551,0.0004682842,0.0002441176,0.0000770837,0.00001471716],"category_scores_gemma":[0.00005100216,0.0001962717,0.00008230846,0.003971234,0.00009339368,0.0004697208,0.0001278042,0.0001713822,0.00000104006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000189155,"about_ca_system_score_gemma":0.00001458514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002126641,"about_ca_topic_score_gemma":0.00007468489,"domain_scores_codex":[0.9985694,0.00003428708,0.0003246174,0.0005805098,0.0001347816,0.0003564138],"domain_scores_gemma":[0.9989231,0.000334114,0.0001003002,0.0004304542,0.0000765611,0.0001355236],"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":[0.00001366998,0.00001143334,0.00030139,0.0000539473,0.0006779548,7.082126e-7,0.0001066607,0.9473457,0.00002082741,0.00006196045,0.0004605662,0.0509452],"study_design_scores_gemma":[0.0001528305,0.00000967951,0.0005075181,0.00001659975,0.002958668,0.000001771673,0.0004803738,0.9926919,0.000001573528,0.001968682,0.0009713377,0.0002390736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00789439,0.001187989,0.9900602,0.00004242753,0.0001115777,0.0001723938,0.00009908011,0.0003939488,0.00003800333],"genre_scores_gemma":[0.9902156,0.0002185456,0.006137773,0.00005233013,0.0001321668,0.00008156474,0.002913009,0.0000406878,0.0002083184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9839224,"threshold_uncertainty_score":0.8003728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05166243762495248,"score_gpt":0.2995868959844977,"score_spread":0.2479244583595452,"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."}}