{"id":"W2267097627","doi":"10.58079/ourl","title":"Some heuristics about local regression and kernel smoothing","year":2013,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; AXA Research Fund","keywords":"Polynomial regression; Mathematics; Polynomial; Principal component regression; Linear regression; Proper linear model; Nonparametric regression; Extension (predicate logic); Kernel regression; Linear model; Applied mathematics; Linear predictor function; Statistics; Regression analysis; Regression; Computer science","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000282171,0.0003467394,0.0004587128,0.00009586222,0.0004277275,0.0002812749,0.0002529607,0.0001998247,0.003456227],"category_scores_gemma":[0.0006611741,0.0002772181,0.00008702696,0.0001979012,0.0003373866,0.004936574,0.0002102079,0.000363668,0.001409695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008235385,"about_ca_system_score_gemma":0.00003887759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009870985,"about_ca_topic_score_gemma":0.00002184778,"domain_scores_codex":[0.9976482,0.0001350159,0.0006130008,0.000531572,0.0006003883,0.0004718243],"domain_scores_gemma":[0.997854,0.000890127,0.0002328228,0.0003494333,0.0002621262,0.0004115133],"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":[0.00004506156,0.0003531892,0.00007423454,0.0002028774,0.00004922404,0.00006040697,0.00009913926,0.000006591616,0.0002659785,0.8625863,0.07367263,0.06258437],"study_design_scores_gemma":[0.001440248,0.0003033588,0.01890222,0.0004731607,0.0001026528,0.00009465656,0.0002671286,0.008028382,0.001442714,0.9517969,0.01636061,0.0007879423],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05365429,0.002072706,0.8514743,0.06262983,0.004740954,0.003371013,0.0009360347,0.001113898,0.020007],"genre_scores_gemma":[0.8040733,0.0008524896,0.1517014,0.03178853,0.002999588,0.0009017379,0.0007612048,0.0001890617,0.006732654],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7504191,"threshold_uncertainty_score":0.999968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02534620422468025,"score_gpt":0.2826220769413938,"score_spread":0.2572758727167135,"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."}}