{"id":"W3175606557","doi":"10.36072/dp.9","title":"Food group diversity and nutrient adequacy: Dietary diversity as a proxy for micronutrient adequacy for different age and sex groups in Mexico and China","year":2021,"lang":"en","type":"report","venue":"","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Canada; Eidgenössisches Departement für Auswärtige Angelegenheiten; Rockefeller Foundation","keywords":"Dietary diversity; Micronutrient; Diversity (politics); Proxy (statistics); Food group; Nutrient; China; Group (periodic table); Geography; Environmental health; Biology; Ecology; Medicine; Mathematics; Sociology; Chemistry; Statistics; Food security; Agriculture; Archaeology; Anthropology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008072355,0.0002658351,0.0002438726,0.001449191,0.0005354134,0.0005578543,0.0004104634,0.000254632,0.001135625],"category_scores_gemma":[0.001068428,0.0001700006,0.0004769767,0.001850049,0.0002171589,0.000371799,0.0007244283,0.0002844038,0.0001868625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008715098,"about_ca_system_score_gemma":0.0007450155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2126192,"about_ca_topic_score_gemma":0.2563705,"domain_scores_codex":[0.999815,0.00004083362,0.00001806742,0.0000371025,0.00004186356,0.00004704728],"domain_scores_gemma":[0.9992577,0.00007857037,0.0002866165,0.00006285612,0.0001592499,0.0001549243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004312458,0.00001946168,0.9975845,0.00001142386,0.00005215199,0.00001805134,0.0002102936,0.00005253897,0.00007784984,0.00004409041,0.0003212015,0.001565385],"study_design_scores_gemma":[0.000001877598,0.00001278516,0.9993345,0.000004362765,0.00001723509,0.00001040468,0.0002506544,0.0000879021,0.00002494232,0.00001145268,0.0002417911,0.000001940041],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932991,0.0003444803,0.0001027983,0.00009578077,0.000007555607,0.00002005072,0.004757822,0.000005032644,0.001367331],"genre_scores_gemma":[0.9935348,0.0002177011,0.0002329839,0.00002162857,0.000006372435,0.00004362954,0.004244121,0.000002882555,0.001695943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2126192,"threshold_uncertainty_score":0.4227632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03945093871813369,"score_gpt":0.2755284292233559,"score_spread":0.2360774905052222,"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."}}