{"id":"W3174710609","doi":"10.1109/i2mtc50364.2021.9460063","title":"Windowing Compensation in Fourier Based Surrogate Analysis","year":2021,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Surrogate data; Autoregressive model; Computer science; Monte Carlo method; Algorithm; Spectral density estimation; Frequency domain; Fourier transform; Mathematics; Statistics; Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004508799,0.0000582413,0.0001228403,0.0002828739,0.00003168218,0.0001431733,0.0001990126,0.00003629689,0.00009911524],"category_scores_gemma":[0.00003898453,0.00005896398,0.00007396299,0.001867553,0.000008018169,0.000343941,0.00006306174,0.0000721297,0.00001550052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002972003,"about_ca_system_score_gemma":0.00008431435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005342068,"about_ca_topic_score_gemma":0.0005024761,"domain_scores_codex":[0.9991425,0.0001601815,0.0001714488,0.0002312242,0.000185496,0.0001091859],"domain_scores_gemma":[0.9993737,0.00009208975,0.00003632436,0.000375404,0.00009148062,0.00003104901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001045416,0.0004947911,0.1930302,0.0000199797,0.0002986995,0.0002403116,0.00383223,0.06509834,0.006791123,0.6852844,0.001075029,0.04382451],"study_design_scores_gemma":[0.0002183997,0.000008270444,0.05035799,0.000004807157,0.00001396282,9.335867e-7,0.00002714692,0.9084506,0.038184,0.001747828,0.0008744175,0.000111652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05221577,0.000009013471,0.938702,0.002341284,0.00002525157,0.00004648245,2.672085e-7,0.0001924904,0.00646743],"genre_scores_gemma":[0.7813756,7.502322e-7,0.2169829,0.001398665,0.000004460778,0.000004538642,0.000008073198,0.000002160294,0.0002228422],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8433523,"threshold_uncertainty_score":0.2404481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739777940461203,"score_gpt":0.2671595805649713,"score_spread":0.2497618011603593,"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."}}