{"id":"W3040900896","doi":"10.1080/01621459.2020.1787840","title":"Semiparametric Estimation of the Distribution of Episodically Consumed Foods Measured With Error","year":2020,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"National Cancer Institute; Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Nonparametric statistics; Estimation; Econometrics; Statistics; Parametric statistics; Computer science; Mathematics; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007948083,0.0006063466,0.001260477,0.001008283,0.0002520471,0.001116549,0.002215911,0.001126829,0.001519007],"category_scores_gemma":[0.05709583,0.0004031209,0.0010054,0.001121275,0.0009281111,0.001162151,0.001784682,0.001512546,0.0003170152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004475882,"about_ca_system_score_gemma":0.00093168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001549439,"about_ca_topic_score_gemma":0.001363601,"domain_scores_codex":[0.9965429,0.00223216,0.0001756159,0.0005306226,0.0004080754,0.0001107185],"domain_scores_gemma":[0.9525631,0.03769115,0.002691346,0.005512002,0.001315796,0.0002266622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004863874,0.0002501571,0.04257867,0.0005764071,0.0006388441,0.0004510982,0.0006680216,0.5096643,0.005761384,0.1561262,0.003769058,0.2790294],"study_design_scores_gemma":[0.00003564509,0.00007932773,0.008382723,0.00005205373,0.00005357017,0.0001704354,0.00006060751,0.9231168,0.001627647,0.06494533,0.001427775,0.00004809498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02357842,0.0001048437,0.9754464,0.0001118377,0.00001108122,0.00003363403,0.00029382,0.000127315,0.0002925425],"genre_scores_gemma":[0.6546355,0.0003432073,0.3400692,0.0001628181,0.00008037239,0.0004096977,0.002098873,0.0001084406,0.002091772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007948083,"threshold_uncertainty_score":0.04203397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01874912685464795,"score_gpt":0.2834844210354261,"score_spread":0.2647352941807782,"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."}}