{"id":"W1573867641","doi":"10.1016/j.foodchem.2012.10.065","title":"Progress with a global branded food composition database","year":2012,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Australian Research Council; Canadian Institutes of Health Research","keywords":"Business; Food industry; Food composition data; Composition (language); Developing country; Database; Food science; Computer science; Biology; Economic growth; Economics","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":[0.01058608,0.00115871,0.001741114,0.01226173,0.000827732,0.004112817,0.003157191,0.001093188,0.01331099],"category_scores_gemma":[0.02028302,0.0005344172,0.001273481,0.01299294,0.0004617271,0.005180101,0.003858045,0.001680214,0.009795454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455566,"about_ca_system_score_gemma":0.005773132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01001269,"about_ca_topic_score_gemma":0.009237786,"domain_scores_codex":[0.9961179,0.0008355079,0.0006804231,0.0007847086,0.001412887,0.0001686103],"domain_scores_gemma":[0.9756383,0.004140998,0.002507929,0.007787884,0.007921125,0.002003858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001155117,0.0005113033,0.05464805,0.002716966,0.0009947363,0.0002313029,0.0004524123,0.002275281,0.00922311,0.01681299,0.2019816,0.7089972],"study_design_scores_gemma":[0.0001009221,0.0001808659,0.04982338,0.0009184222,0.0007938468,0.000406752,0.0003632997,0.004734259,0.007929646,0.0124744,0.9221604,0.000113839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.05931885,0.01542645,0.1598803,0.008229917,0.001795455,0.000559898,0.7052948,0.01126127,0.03823309],"genre_scores_gemma":[0.0571504,0.00873155,0.1569746,0.001859142,0.0003779647,0.0004550309,0.765788,0.001478316,0.007185059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01331099,"threshold_uncertainty_score":0.05598521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713838713052522,"score_gpt":0.266052158815644,"score_spread":0.2489137716851188,"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."}}