{"id":"W3038982663","doi":"10.1109/tit.2020.3007406","title":"Nonparametric Specification Testing for Signal Models","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Mathematical Analysis and Transform Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Narodowe Centrum Nauki","keywords":"Nonparametric statistics; Statistical hypothesis testing; Parametric statistics; Kernel (algebra); SIGNAL (programming language); Algorithm; Mathematics; False alarm; Ratio test; Sampling (signal processing); Computer science; Statistical power; Kernel method; Statistics; Artificial intelligence; Discrete mathematics; Support vector machine","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":[0.0009459587,0.0001778191,0.0002746717,0.0002241446,0.0002299461,0.00008102228,0.0001740721,0.0000957785,0.0003359992],"category_scores_gemma":[0.0002041804,0.0001512884,0.000224961,0.000738024,0.00004008899,0.0009019612,7.202598e-7,0.0001956405,0.0001178674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003794934,"about_ca_system_score_gemma":0.00003443039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001184313,"about_ca_topic_score_gemma":3.145615e-7,"domain_scores_codex":[0.9985586,0.00009148313,0.0007011449,0.0001473115,0.0002976636,0.0002038214],"domain_scores_gemma":[0.9968362,0.002396432,0.0001990217,0.0001977371,0.0002318093,0.0001387295],"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":[0.0002760952,0.0001975901,4.141344e-7,0.0005459083,0.0001409263,2.04124e-7,0.002895951,0.03026647,0.0003301417,0.7045198,0.0002806349,0.2605458],"study_design_scores_gemma":[0.0005154771,0.0001746672,0.000001185493,0.0000264731,0.0001406676,0.000002195452,0.0003758869,0.4056052,0.01238607,0.5801942,0.0003903013,0.0001877101],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000830688,0.000004703336,0.9864476,0.0003418252,0.00006864053,0.0005969418,0.00006434334,0.0002051128,0.01144013],"genre_scores_gemma":[0.753481,0.000004658507,0.2455626,0.0005870659,0.00005322932,0.0001804523,0.000008541622,0.00002007909,0.0001024367],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7526503,"threshold_uncertainty_score":0.6169361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1462756064590311,"score_gpt":0.3273261080644624,"score_spread":0.1810505016054313,"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."}}