{"id":"W4417516663","doi":"10.48550/arxiv.2505.07283","title":"On Data Sharpening in Nonparametric Autoregressive Models","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Sharpening; Autoregressive model; Bivariate analysis; Nonparametric statistics; Nonparametric regression; Regression; Nonlinear system","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008476227,0.0003651585,0.0007267379,0.0003880017,0.00005491008,0.0000641576,0.001499341,0.0003784304,0.000210387],"category_scores_gemma":[0.0129682,0.000322905,0.00007446633,0.0003528247,0.00006560895,0.00009132313,0.002820367,0.001356901,0.00006105029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001242221,"about_ca_system_score_gemma":0.0002393788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001662669,"about_ca_topic_score_gemma":0.00002022535,"domain_scores_codex":[0.9974035,0.0002574924,0.0006323171,0.0009996416,0.0003320012,0.0003750309],"domain_scores_gemma":[0.9918721,0.005443364,0.000304037,0.002182248,0.0001037268,0.00009457311],"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":[0.0001092301,0.0006445543,0.02812157,0.001761155,0.0002221688,0.0002011833,0.0009228301,0.002564788,0.00002102236,0.917491,0.009440175,0.03850032],"study_design_scores_gemma":[0.0002688142,0.00003374178,0.006499883,0.001653069,0.00006794221,8.452623e-7,0.00002622623,0.1845199,0.0000767123,0.8064289,0.00007614489,0.0003478259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3480683,0.0003534207,0.6080862,0.0003122113,0.001296654,0.00101008,0.001453692,0.0002050829,0.03921442],"genre_scores_gemma":[0.5587261,0.00009452837,0.4390139,0.0003228917,0.0001328494,0.000115102,0.0001728642,0.00004616243,0.001375629],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2106578,"threshold_uncertainty_score":0.9999223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4261190316472943,"score_gpt":0.4607238833631164,"score_spread":0.03460485171582217,"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."}}