{"id":"W2887146283","doi":"10.1017/s000711451800199x","title":"Predicting serum vitamin D concentrations based on self-reported lifestyle factors and personal attributes","year":2018,"lang":"en","type":"article","venue":"British Journal Of Nutrition","topic":"Vitamin D Research Studies","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; McGill University Health Centre; Université de Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Canadian Institutes of Health Research; Cancer Research Society; McGill University","keywords":"Vitamin D and neurology; Epidemiology; Medicine; Population; Linear regression; Regression analysis; Lasso (programming language); Demography; Gerontology; Internal medicine; Physiology; Endocrinology; Environmental health; Mathematics; Statistics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001507995,0.0004416876,0.0003209928,0.0005798377,0.000139154,0.0004887923,0.0003417973,0.0002977994,0.0007396596],"category_scores_gemma":[0.00417906,0.0001822151,0.0005576076,0.0004790321,0.0001413799,0.0001887116,0.0002690091,0.0003943022,0.0001980244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003201826,"about_ca_system_score_gemma":0.000407672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02523881,"about_ca_topic_score_gemma":0.03468504,"domain_scores_codex":[0.9995391,0.0002138318,0.0000293283,0.0001188918,0.00006109121,0.00003768177],"domain_scores_gemma":[0.9984003,0.001003598,0.0002609812,0.0001142575,0.0001502604,0.00007065215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000141176,0.00005743392,0.9878748,0.00002016709,0.0001404005,0.00002338094,0.00003715239,0.0031018,0.000591315,0.0000433303,0.0001425753,0.007826538],"study_design_scores_gemma":[0.00003331033,0.0002880198,0.8798135,0.00001355281,0.0001590024,0.0001049934,0.00007079281,0.1177112,0.0008627275,0.0003866345,0.0005417264,0.00001460185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914353,0.0002895833,0.006940224,0.00009089863,0.000006911211,0.00003080904,0.0008195972,0.00005560084,0.0003311156],"genre_scores_gemma":[0.9950631,0.0000987387,0.003888776,0.00002000088,0.000005960489,0.00001895279,0.0005536923,0.000004564869,0.0003462908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02523881,"threshold_uncertainty_score":0.05018383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0244210963922814,"score_gpt":0.2875794406925137,"score_spread":0.2631583443002323,"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."}}