{"id":"W2127244244","doi":"10.1007/s00394-007-0691-6","title":"Minimising the population risk of micronutrient deficiency and over-consumption: a new approach using selenium as an example","year":2008,"lang":"en","type":"article","venue":"European Journal of Nutrition","topic":"Selenium in Biological Systems","field":"Nursing","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Micronutrient; Environmental health; Dietary Reference Intake; Population; Reference Daily Intake; Risk assessment; Medicine; Incidence (geometry); Allowance (engineering); Consumption (sociology); Risk analysis (engineering); Micronutrient deficiency; Operations management; Nutrient; Computer science; Mathematics; Engineering; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001357562,0.001141922,0.00117125,0.001046719,0.0006570663,0.001819846,0.001232808,0.002560169,0.002245035],"category_scores_gemma":[0.001918933,0.0002201256,0.0007618691,0.0005093776,0.001411761,0.002178683,0.001508543,0.00213545,0.0002832939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006632759,"about_ca_system_score_gemma":0.00126465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585009,"about_ca_topic_score_gemma":0.003520809,"domain_scores_codex":[0.9994739,0.0002320104,0.00004063496,0.00007946567,0.0001458512,0.00002815676],"domain_scores_gemma":[0.9992329,0.0004083778,0.00007309316,0.00005829657,0.000163771,0.00006346121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008593946,0.00155108,0.007964718,0.003546465,0.0008734781,0.002590162,0.002285217,0.02148973,0.02477902,0.156556,0.02167168,0.755833],"study_design_scores_gemma":[0.0004980665,0.006181751,0.01241139,0.002190917,0.001488239,0.009210602,0.006381541,0.07669519,0.02444685,0.5915149,0.2685841,0.0003963789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1042685,0.07137691,0.5752926,0.1717039,0.00610631,0.0003478416,0.000216537,0.0006903126,0.06999704],"genre_scores_gemma":[0.5376348,0.05665337,0.3722504,0.009222822,0.002625047,0.0003095512,0.00008569733,0.00008561564,0.02113266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002560169,"threshold_uncertainty_score":0.007510424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09598703371867805,"score_gpt":0.280916161037183,"score_spread":0.1849291273185049,"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."}}