{"id":"W3185589360","doi":"10.36227/techrxiv.15000447.v1","title":"The Relation Among Obesity and Sugar Consumption: A Machine Learning Approach","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Obesity; Sugar; Relation (database); Data set; Calorie; Consumption (sociology); Gram; Sugar consumption; Set (abstract data type); Food science; Mathematics; Computer science; Statistics; Medicine; Biology; Data mining; Endocrinology; Social science; Sociology","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":[0.003500916,0.0008244051,0.0007315044,0.004927977,0.00059519,0.001437976,0.0007841038,0.001283414,0.002180798],"category_scores_gemma":[0.01218658,0.0002730185,0.0009760625,0.003845537,0.0006615758,0.00121357,0.0006107925,0.001773352,0.0004497122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006901309,"about_ca_system_score_gemma":0.0006031306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005565967,"about_ca_topic_score_gemma":0.003107897,"domain_scores_codex":[0.997857,0.001355877,0.0001024981,0.0002860147,0.0002599588,0.0001385766],"domain_scores_gemma":[0.9827145,0.01583784,0.0004741134,0.0003564587,0.0004419202,0.000175291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00142208,0.003473824,0.5728526,0.0005479392,0.001313487,0.001150711,0.0006313992,0.1573136,0.00323599,0.01467178,0.005303634,0.2380829],"study_design_scores_gemma":[0.0001008589,0.0004370295,0.1347639,0.00006755782,0.0001987804,0.000349273,0.0003824888,0.8307323,0.001439691,0.02927204,0.002187739,0.00006818067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7787179,0.005293855,0.1986533,0.006129747,0.0002664735,0.0002773428,0.0030605,0.0006356009,0.006965226],"genre_scores_gemma":[0.9495478,0.0008477888,0.04658548,0.0002666912,0.0002707451,0.0001342583,0.001352507,0.00002661765,0.0009680233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005565967,"threshold_uncertainty_score":0.01851481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03142218109604167,"score_gpt":0.2649265284206356,"score_spread":0.233504347324594,"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."}}