{"id":"W4315643872","doi":"10.1016/j.spl.2023.109791","title":"Adaptive signal recovery with Subbotin noise","year":2023,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Noise (video); Variable (mathematics); Selection (genetic algorithm); Algorithm; Hamming distance; Sequence (biology); Feature selection; Statistics; Pattern recognition (psychology); Artificial intelligence; Computer science; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009729409,0.0002815784,0.0004051234,0.00009222256,0.000142671,0.00006669271,0.0002301763,0.00006895798,0.0003936086],"category_scores_gemma":[0.002346577,0.0002295913,0.00005579157,0.0004659815,0.0003910823,0.00007961264,0.00008921378,0.00032026,0.0001850548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205673,"about_ca_system_score_gemma":0.0001052873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006331116,"about_ca_topic_score_gemma":0.00006157374,"domain_scores_codex":[0.9975801,0.0003432157,0.0004706499,0.0005390511,0.0005009121,0.0005660712],"domain_scores_gemma":[0.9943029,0.004714174,0.0001612725,0.000464729,0.0001860736,0.0001708222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003047697,0.0001463565,0.001965176,0.0003108082,0.00009423361,0.0001013404,0.0004489603,0.00004596944,0.001202598,0.9391135,0.03436366,0.02190262],"study_design_scores_gemma":[0.0003547851,0.0003631558,0.008272182,0.00006581794,0.00006504608,0.000004754781,0.00002098073,0.001835479,0.0002623142,0.98826,0.0001544829,0.0003410285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08373121,0.000002435916,0.9131054,0.0007895093,0.0001267104,0.0005436242,0.0008143611,0.0002429866,0.0006437514],"genre_scores_gemma":[0.02050303,0.000003354007,0.9785464,0.0005476171,0.00007503227,0.00009518537,0.00004166655,0.0000462623,0.0001414728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06544097,"threshold_uncertainty_score":0.9362463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08071340939533503,"score_gpt":0.3199548768790832,"score_spread":0.2392414674837482,"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."}}