{"id":"W4387178925","doi":"10.3390/e25101394","title":"Lossless Transformations and Excess Risk Bounds in Statistical Inference","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Lossless compression; Mathematics; Transformation (genetics); Statistic; Nonparametric statistics; Test statistic; Inference; Applied mathematics; Statistical hypothesis testing; Computer science; Statistics; Algorithm; Data compression; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02003874,0.001813311,0.001904157,0.00280275,0.0008949827,0.003521347,0.002513772,0.002458479,0.002737916],"category_scores_gemma":[0.1068199,0.0007862542,0.001518483,0.002284953,0.01089626,0.009967041,0.005565637,0.00754291,0.0006647834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002768994,"about_ca_system_score_gemma":0.001283314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006155998,"about_ca_topic_score_gemma":0.0003329244,"domain_scores_codex":[0.9884763,0.005880885,0.0006701846,0.001726337,0.002655368,0.0005909436],"domain_scores_gemma":[0.8643116,0.1189222,0.005006217,0.00740241,0.003117592,0.001240062],"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":[0.000133975,0.00007638384,0.002060894,0.0002041015,0.0001033716,0.0002718455,0.0002302897,0.1038021,0.001613361,0.8667535,0.001156446,0.02359391],"study_design_scores_gemma":[0.00001430635,0.00008416372,0.0003984924,0.00005227673,0.00001935228,0.0001107684,0.0000299474,0.1937154,0.001155261,0.8034806,0.0009201519,0.0000192538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01895569,0.001249211,0.9743633,0.001443329,0.00007150353,0.00003334671,0.000132845,0.0001541138,0.003596614],"genre_scores_gemma":[0.7727358,0.002892738,0.2158643,0.001335255,0.0008760839,0.0005134637,0.0006409213,0.0004357535,0.004705636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02003874,"threshold_uncertainty_score":0.1059762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.066050946835071,"score_gpt":0.4003766839143775,"score_spread":0.3343257370793065,"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."}}