{"id":"W4376484132","doi":"10.1214/23-ss143","title":"White noise testing for functional time series","year":2023,"lang":"en","type":"article","venue":"Statistics Surveys","topic":"Mental Health Research Topics","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"White noise; Residual; Context (archaeology); Uncorrelated; Computer science; Noise (video); Series (stratigraphy); Goodness of fit; Time domain; Statistics; Algorithm; Mathematics; Econometrics; Artificial intelligence; Machine learning","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002371092,0.0001051007,0.000140113,0.0001072355,0.0002202688,0.00002960168,0.000105395,0.00006071922,0.002665948],"category_scores_gemma":[0.001279296,0.0001097391,0.00002055558,0.0003771083,0.00008017293,0.00004771306,0.00005584919,0.0001368423,0.004276455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005331479,"about_ca_system_score_gemma":0.0001051699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000103497,"about_ca_topic_score_gemma":0.00009361521,"domain_scores_codex":[0.9982486,0.0004822965,0.0002454267,0.0002562544,0.000250454,0.0005169474],"domain_scores_gemma":[0.9972603,0.002094013,0.00006091203,0.0002151367,0.0002273839,0.0001421979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001579733,0.0001009031,0.1596327,0.0003105231,0.00006486866,0.00008006729,0.0005143904,0.00005585696,0.0001855557,0.03171116,0.7276764,0.0795096],"study_design_scores_gemma":[0.0005588618,0.000386473,0.9750947,0.0000124878,0.000006458374,0.000008707721,0.0001109348,0.002026571,0.00001050361,0.01016106,0.01147038,0.000152886],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.04430429,0.0001314874,0.8056825,0.002579752,0.008388821,0.003402378,0.03283742,0.001325432,0.1013479],"genre_scores_gemma":[0.07786398,0.00001248978,0.1382975,0.0005185717,0.001610448,0.001100619,0.008417833,0.0002254491,0.7719531],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.815462,"threshold_uncertainty_score":0.9982458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1795628250244119,"score_gpt":0.4273315839492192,"score_spread":0.2477687589248072,"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."}}