{"id":"W4220750305","doi":"10.1016/j.jand.2022.03.010","title":"Using Short-Term Dietary Intake Data to Address Research Questions Related to Usual Dietary Intake among Populations and Subpopulations: Assumptions, Statistical Techniques, and Considerations","year":2022,"lang":"en","type":"article","venue":"Journal of the Academy of Nutrition and Dietetics","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Statistical inference; Percentile; Statistics; Inference; Term (time); Estimation; Variance (accounting); Skewness; Econometrics; Population; Mathematics; Medicine; Computer science; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009688113,0.0001352385,0.0003499296,0.0003853348,0.001028428,0.00004523201,0.0001430143,0.00009787313,0.00003670548],"category_scores_gemma":[0.0006625554,0.0001172015,0.00004710116,0.0004439077,0.0003416278,0.000206563,0.0006867636,0.0008516778,2.464674e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020841,"about_ca_system_score_gemma":0.00005305001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004887554,"about_ca_topic_score_gemma":0.00003303002,"domain_scores_codex":[0.9978907,0.0002978451,0.0007576746,0.0002471483,0.000606099,0.0002005928],"domain_scores_gemma":[0.9986836,0.0004278044,0.000160915,0.0001910198,0.000258814,0.0002778237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002407,0.006209423,0.6235452,0.001563378,0.001114096,0.0002295431,0.002097235,0.0007703327,0.0411819,0.05358764,0.2563674,0.01092689],"study_design_scores_gemma":[0.001052994,0.0007712086,0.9640684,0.0007762929,0.0003829128,0.001115303,0.001274736,0.0007289339,0.00007888549,0.02209134,0.007480854,0.0001780959],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9525097,0.004239225,0.0001475554,0.04069462,0.0001347267,0.0009894406,0.001220548,0.00001867013,0.00004553611],"genre_scores_gemma":[0.9682072,0.001883324,0.02920322,0.0004038105,0.0001483138,0.00003109636,0.00008500281,0.00001711373,0.00002086776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3405232,"threshold_uncertainty_score":0.7909944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3560173769722558,"score_gpt":0.4723956329173493,"score_spread":0.1163782559450935,"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."}}