{"id":"W4256593800","doi":"10.1017/s1431927619001600","title":"Removing Stripes, Scratches, and Curtaining with Non-Recoverable Compressed Sensing","year":2019,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Compressed sensing; Materials science; Computer science; Geology; 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":[0.0001549906,0.0002329498,0.0003793137,0.0001301492,0.0001451316,0.0001674548,0.00007037577,0.00006669286,0.00003406908],"category_scores_gemma":[0.000006657842,0.0002158339,0.00004157146,0.0002335178,0.00005475846,0.0001934012,0.0000341815,0.0002150559,0.000008094593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004347426,"about_ca_system_score_gemma":0.00002416364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002093195,"about_ca_topic_score_gemma":0.00002913713,"domain_scores_codex":[0.9990168,0.00001601005,0.0001999633,0.000319044,0.00008654497,0.0003616539],"domain_scores_gemma":[0.9995352,0.00008150343,0.00004650324,0.0002144813,0.0000335329,0.00008879232],"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":[0.00002110722,0.000006720541,0.006749386,0.0001502458,0.0002349109,0.00001093035,0.0005580677,0.004629001,0.9825014,0.000003622132,0.0002289325,0.004905662],"study_design_scores_gemma":[0.002399527,0.00007279751,0.002824426,0.0006780647,0.0008155569,0.0001812537,0.00272907,0.5516325,0.4325469,0.00006074209,0.004921476,0.001137782],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9497769,0.002553775,0.04585096,0.00002339622,0.00006678629,0.0001297759,0.00001578679,0.00009772531,0.001484892],"genre_scores_gemma":[0.9832369,0.0003536712,0.01588777,0.0001012543,0.00002647038,0.00000121132,0.00001829739,0.00003670586,0.0003376991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5499546,"threshold_uncertainty_score":0.880145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003343271322918802,"score_gpt":0.1901184968079171,"score_spread":0.1867752254849983,"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."}}